Systems and methods for processing photos with geographical segmentation

ABSTRACT

Systems and methods are provided to process a digital photo. An apparatus to process digital photos can include a tangibly embodied computer processor (CP) and a tangibly embodied database. The CP can perform processing including: (a) inputting a photo from a user device, and the photo including geographic data that represents a photo location at which the photo was generated; (b) comparing at least one area with the photo location and associating an area identifier to the photo as part of photo data; and (c) performing processing based on the area identifier and the photo data.

RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application Ser. No. 62/940,415 filed Nov. 26, 2019, the entire disclosure of which is hereby incorporated by reference.

BACKGROUND

This systems and methods described herein relate to processing photos, and in particular to processing photos in a geographical area.

Photography is popular with a wide variety of people. Photography can include taking pictures of points of interest, activities of interest, “selfies”, and innumerable other items. Photography can include taking a picture with a camera or other device that is dedicated to photography. Photography can include taking a picture with a smart phone, cell phone, or other user device that provides picture taking abilities as well as various other abilities and uses. Websites and other electronic resources exist that provide the ability to upload or otherwise save pictures that have been taken by a person. Such websites can allow a user to access pictures and perform other manipulation of pictures. However, known technology is lacking in capabilities that such technology provides. The systems and methods of the disclosure address shortcomings that exist with known technology.

SUMMARY OF THE DISCLOSURE

Systems and methods are provided to process a digital photo and to segment a geographical area. An apparatus to process digital photos can include a tangibly embodied computer processor (CP) and a tangibly embodied database, the CP implementing instructions on a non-transitory computer medium disposed in the database, and the database in communication with the CP.

The apparatus can include (A) a communication portion for providing communication between the CP and an electronic user device; (B) the database that includes the non-transitory computer medium, and the database including the instructions, and (C) a cascading framework that includes framework areas, and the framework areas include: first level areas, and each of the first level areas divided into second level areas, the second level areas being divided into third level areas; and (D) the CP. The CP can perform processing including: (a) inputting a photo from the user device, and the photo including geographic data that represents a photo location at which the photo item was generated; (b) comparing the first level area, of the first level areas, in which the photo location is located and associating a first level area identifier to the photo as part of the photo data; (c) comparing the photo location with the second level areas to determine a second level area in which the photo location is located and associating a second level area identifier to the photo as part of the photo data; (d) comparing the photo location with the third level areas to determine a matching third level area in which the photo location is located and associating a third level area identifier to the photo as part of the photo data; (e) assigning the photo to the matching third level area; and (f) performing photo processing, and the photo processing including aggregating a photo count of the matching third level area.

In accordance with principles of the disclosed subject matter, the disclosure provides systems and methods to perform geographic identification of an area combined with using a photo, which is associated with the area, as a vote for one or more popularity determinations of the geographic area. The geographic area can be used for a variety of other purposes. The geographic area and/or a photo associated with the geographic area can be tagged so as to associate content or attributes to the geographic area and/or to the photo.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed subject matter of the present application will now be described in more detail with reference to exemplary embodiments of the apparatus and method, given by way of example, and with reference to the accompanying drawings, in which:

FIG. 1 is a diagram showing a photo ecosystem or ecosystem in accordance with at least one embodiment of the disclosed subject matter.

FIG. 2 is a high level flowchart illustrating processing that can be performed by the photo system (PS), in accordance with principles of the disclosed subject matter.

FIG. 3 is a flowchart showing further details of area segmentation processing, in accordance with principles of the disclosed subject matter.

FIG. 4 is a flowchart showing details of “processing is performed to assign area identifier and identify boundaries of the area” in accordance with principles of the disclosed subject matter.

FIG. 5 is a diagram showing segmentation of a remote segmentation area, in accordance with principles of the disclosed subject matter.

FIG. 6 is a flowchart showing details of the “photo system performs photo input processing”, in accordance with principles of the disclosed subject matter.

FIG. 7 is a flowchart showing further details of the system “processes current photo so as to integrate such photo into the system” in accordance with principles of the disclosed subject matter.

FIG. 8 is a flowchart showing processing “to place a photo into a virtual container,” in accordance with principles of the disclosed subject matter.

FIG. 9 is a flowchart showing further details of the “system performs spot generation processing”, in accordance with principles of the disclosed subject matter.

FIG. 10 is a flowchart showing in further detail the “system watches for an event to be observed that invokes spot generation processing” in accordance with principles of the disclosed subject matter.

FIG. 11 is a flowchart showing further details of a “spot generation processing” subroutine, in accordance with principles of the disclosed subject matter.

FIG. 12 is a flowchart showing in further detail the system performs “type tagging” for a current patch, in accordance with principles of the disclosed subject matter.

FIG. 13 is a flowchart showing further details of the system performs “type tagging” for a current spot for a type-A photo, in accordance with at least one embodiment of the disclosed subject matter.

FIG. 14 is a flowchart showing in further detail the system performs “user engagement processing,” invoked or called upon from FIG. 2, in accordance with principles of the disclosed subject matter.

FIG. 15 is a flowchart showing in further detail the photo system “interfaces with a user device to input a new photo”, in accordance with principles of the disclosed subject matter.

FIG. 16 is a flowchart showing in further detail the “photo system interfaces with a user to input attributes of a photo” in accordance with principles of the disclosed subject matter.

FIG. 17 is a flowchart showing in further detail the “photo system interfaces with a user device to perform photo user processing, in accordance with principles of the disclosed subject matter.

FIG. 18 is a diagram that includes a GUI (graphical user interface) that illustrates aspects of “photo info” or “photo information” processing, in accordance with principles of the disclosed subject matter.

FIG. 19 is a diagram that includes a GUI that can be presented to a user or potential user in conjunction with the user signing on or logging into photo system, in accordance with principles of the disclosed subject matter.

FIG. 20 is a diagram that includes a GUI 2000 that can be utilized so as to launch or invoke processing in accordance with principles of the disclosed subject matter.

FIG. 21 is a diagram that includes a GUI 2100 that can be utilized so as to interface with the user so as to input selection of search filters, in accordance with principles of the disclosed subject matter.

FIG. 22 is a diagram that includes a GUI 2200 that can be utilized to provide the user “spots around me” functionality in accordance with principles of the disclosed subject matter.

FIG. 23 is a diagram that includes a GUI 2300 that can be utilized to provide the user information regarding a “site”, with such site including a plurality of spots, in accordance with principles of the disclosed subject matter.

FIG. 24 is a diagram that includes a GUI 2400 that illustrates “spots around me” functionality, in accordance with principles of the disclosed subject matter.

FIG. 25 is a diagram that includes a GUI 2500 that can be presented to the user to provide additional information to the user regarding spots around the user or user device, in accordance with principles of the disclosed subject matter.

FIG. 26 is a diagram that includes a GUI 2600 that shows further details of “spots around me” functionality in accordance with principles of the disclosed subject matter.

FIG. 27 is a diagram that includes a GUI 2700 that can be presented to the user to provide various menu selection items in accordance with principles of the disclosed subject matter.

FIG. 28 is a diagram that includes a GUI 2800 that can provide various information regarding one or more spots, in accordance with principles of the disclosed subject matter.

FIG. 29 is a diagram that includes a GUI 2900 that can provide tagging of photos or allowing users to identify additional content that can be added to or associated with a photo, for example, for future search options in accordance with principles of the disclosed subject matter.

FIG. 30 is a diagram that includes a GUI 3000 that can be displayed as a result of the user tapping or selecting a suitable button for further tagging of photos or allowing users to identify additional content that can be added to or associated with a photo, for example, in accordance with principles of the disclosed subject matter.

FIG. 31 is a diagram that includes a GUI 3100 for further tagging of photos or allowing users to identify additional content, which can be added to or associated with a photo, that can be generated by the photo system and presented to the user, in accordance with principles of the disclosed subject matter.

FIG. 32 is a diagram that includes a GUI 3200 for further tagging of photos or allowing users to identify additional content, which can be added to or associated with a photo, that can be generated by the photo system and presented to the user, in accordance with principles of the disclosed subject matter.

FIG. 33 is a diagram that includes a GUI 3300 for further tagging of photos or allow users to identify additional content, which can be added to or associated with a photo, that can be generated by the photo system and presented to the user, in accordance with principles of the disclosed subject matter.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

A few inventive aspects of the disclosed embodiments are explained in detail below with reference to the various figures. Exemplary embodiments are described to illustrate the disclosed subject matter, not to limit its scope, which is defined by the claims. Those of ordinary skill in the art will recognize a number of equivalent variations of the various features provided in the description that follows.

Locations exist that are popular for a variety of reasons and characteristics. Locations can be popular amongst local residents or travelers. Locations can be popular for sightseeing, taking “selfies”, taking photographs, or partaking in interesting activities. Interesting activities can include “seeing (taking pictures), eating, drinking, shopping, and various other activities including conceptual things. Conceptual things can include ideas or referendums, for example. A particular location can be “popular” or locations may become “popular” as a function of time. Locations that become popular as a function of time can be dependent on seasonal events, times of day, newsworthy developments that are related to such location, and trending items that are related to the particular patient.

However, visitors, travelers, and even local residents may not always be aware of these popular locations. A popular location can be marked with a physical sign or other marker so as to notify interested persons of the particular location. Also, some of these popular locations are identified on maps or in travel guides. However, such information is not always readily available to interested travelers or other persons. Such information may also become outdated. Further, signs and notifications may not provide information about the location that helps an interested person to determine appealing characteristics or other features of interest regarding the location.

It is one objective of the present disclosure to provide information by graphic display on a networked computer, mobile device or other processing system that a user can access to identify locations of interest. Such locations of interest can include popular locations. The information can be used for planning purposes when a user is planning a visit to the location of interest or near the location of interest. Another objective of the present disclosure is to provide a method and system for processing photos that determine popularity or registers a user vote of preference; and the popularity of a location or area can be identified by being referred to as a “spot”. Thus, an area that has a predetermined density of photos can be deemed a “spot” by the PS (photo system) of the disclosure. Thus, for example, a “patch” (as described below) that has a predetermined density of photos can be deemed a “spot” by the PS. Other types of areas, e.g. a “local” may also be deemed a “spot”. The method and system can determine the popularity of an area, for example the popularity of a patch, using various characteristics or attributes of interest, which can be associated with the area in a suitable database. Once identified as a “spot”, the database can contain information that identifies the spot, conveys information regarding the particular location of the spot, and includes various other attributes of the spot. It is a further objective of the present disclosure to provide the user with photos previously captured at the spot in order to assist the user in determining if the characteristics of the spot are of interest. Another objective of the disclosure is to utilize user supplied content and preferences to assist in a determination in identification of “trusted critics” by establishing user power ratings by area of interest. Such user power ratings can include the establishment of user dashboards highlighting volume of activity, concentration of followers, areas of interest, and geographical proclivity. A further objective is to provide users the ability to identify and organize under “affinity groups.” A further objective is to provide users the ability of “filtered following” that organizes content based upon user interests, preferences including trusted critics, affinity groups, geography, and other attributes. It is a further objective to allow users the ability to flexibly organize and reorganize content and perspectives, preferences, user following and/or affinity groups to validate or more generally perform an “assessment” of the popularity or other attribute of a spot.

It is a further objective of the present disclosure to provide a system that supplies popular spot characteristics and other information to a user that is dynamically updated over time so as to be of increased relevance to the user. It is a further objective of the present disclosure to provide information to the user regarding a spot that is specifically customized to the user.

The systems and methods of the disclosure can provide the above objectives and can provide various other features as described in detail below. The system of the disclosure can include a computer, computer system or machine that can be in the form of or include one or more computer processors “CPs” and one or more databases. The computer can include or be in the form of or be connected to a network server. Processing can be performed that includes accessing a photo database containing photos, i.e. digital photos, and location data and determining one or more clusters of the digital photos based on the location data. Processing can further include associating time data and a variety of other data appended to the photo or cluster of photos. Processing can be performed to determine a popular spot location for representing the digital photo or cluster of digital photos and to generate results, which can be stored in a database for later access.

In at least one embodiment of the disclosure, the process of determining a popular location, herein referred to as a “spot”, can begin with geographic segmentation of the globe or some other area. Geographic segmentation can include the establishment of uniquely identifiable areas, which can vary in size. The largest areas can be as large as the Pacific Ocean or the Antarctic. The smallest areas can be a point or an area of a few square feet. A smallest area, of the uniquely identifiable areas, can correspond to what is herein referred to as a “patch”. A “patch” can become a “spot”, i.e. a “patch-spot” if density of photos in the patch is sufficient. The methodology of the disclosure can initially establish larger location areas that are, for example, approximately 100 miles×100 miles in area. The smallest area that can be referred to as a patch, can be approximately 13 feet×13 feet. However, as described below, it should be appreciated that the particular areas processed, including the size of such areas, can vary in implementation of a system of the invention.

Geographic segmentation of an area under consideration, such as the world or globe, can start with a desired segmentation, such as the 100 mile×100 mile segmentation. Such areas that have been formed by segmentation, can be referred to as first level areas. Each of the first level areas can be divided into second level areas. Each of the second level areas can further be divided into third level areas. Further segmentation can be provided. The particular area to be processed, be it the world or some smaller area such as a trade show venue, can vary as desired. Additionally, the number of levels provided can vary as desired, as well as size of each of the areas.

Accordingly, the particular number of levels of areas, size of the areas, and other attributes of areas as described herein are for purposes of illustration. The number of levels of areas can be varied as desired, the size of the areas can be varied as desired, the shape of the areas can be varied as desired, other attributes of the areas can be varied as desired, interrelationship between the areas can be varied as desired, and/or other aspects of geographic segmentation can be varied as desired. The size and shape of the area that constitutes or includes a spot can be varied as desired. The sizes as described herein are approximate and may well vary within thresholds. Such thresholds may include variance, of the size of the areas, by + or −5%, + or −10%, + or −15%, + or −20%, for example.

For example, geographic segmentation areas or areas can be generally standardized into 6 size categories, in accordance with at least one embodiment of the disclosure. The segmentation areas can include 6 size categories. The 6 size categories can illustratively include:

(1) “Remote”: 100 miles by 100 miles (19,700 across the earth);

(2) “Territory”: 10 miles by 10 miles (1.97 Million (M) spots across the earth);

(3) “Sector”: 1 mile by 1 mile (1 spot per square (sq) mile—197 M spots across the earth);

(4) “Quadrant”: ¼ mile by ¼ mile (1,340 ft by 1,340 ft—16 spots per square mile 3.15 Billion (B) across earth); and

(5) “Local”: 134 ft by 134 ft (1,600 spots per square mile—315 B across the earth).

(6) “Patch”: 13.4 foot by 13.4 foot areas (160,000 per square mile—31.5 trillion across the earth).

Accordingly, the remote areas can constitute first level areas, the territory areas can constitute second level areas, the sector areas can constitute third level areas, the quadrant areas can constitute fourth level areas, the local areas can constitute fifth level areas, and the patch areas can constitute sixth level areas. Accordingly, the largest of the areas can be the remote areas. The smallest of the areas can be the patch areas. The above naming or nomenclature is used for purposes of explanation and discussion herein. It should of course be appreciated that the areas can be named as desired.

As described herein, the areas as defined and processed in the system of the disclosure can be formed by various techniques and mechanisms. Area boundaries for each remote area, for example, can be established using longitude-latitude data. Various information can be used to determine the boundaries of the remote areas and/or to determine the longitude-latitude of a particular location or geographical feature. Such information can include natural landmass orientation boundaries, ocean or water boundaries, concentrations of populations, countries, states, provinces, counties, cities and other predefined sites or areas.

Once the first level areas are defined with boundaries of each of the “remote” can be defined using a 100 mile×100 mile, the second level areas (territories) can then be defined. The boundaries of each of the “territories” can be defined using a 10 mile×10 mile grid system that can be used for further tagging or identifying content for example. That is, the system of the disclosure can segment each of the “remote” areas by mathematically deriving longitudes and latitudes for each territory, i.e., such that each territory possesses a 10 mile×10 mile area.

Once the second level areas are defined, the third level areas (sectors) can then be defined. The boundaries of each of the sectors can be defined using a 1 mile×1 mile area grid system that can be used for further tagging or identifying content. That is, the system of the disclosure can segment each of the territory areas by mathematically deriving longitudes and latitudes for each sector, i.e., such that each sector possesses a 1 mile×1 mile area.

Once the third level areas are defined, the fourth level areas (quadrants) can then be defined. The boundaries of each of the quadrants can be defined using a ¼ mile×¼ mile grid system that can be used for further tagging or identifying content. That is, the system of the disclosure can segment each of the quadrant areas by mathematically deriving longitudes and latitudes for each quadrant, i.e., such that each quadrant possesses a ¼ mile×¼ mile area, i.e. a 1,340 feet×1,340 feet area.

Once the fourth level areas are defined, the fifth level areas (locals) can then be defined. The boundaries of each of the locals can be defined using a 134 feet×134 feet grid system that can be used for further tagging or identifying content, i.e. by breaking up each of the quadrants by using a 10×10 grid. That is, the system of the disclosure can segment each of the local areas by mathematically deriving longitudes and latitudes for each local, such that each local possesses a 134 feet×134 area.

Once the fifth level areas are defined, the sixth and lowest level areas (i.e. patches) can then be defined. The boundaries of each of the patches can be defined using a 13.4 feet×13.4 feet grid system that can be used for further tagging or identifying content, i.e. by breaking up each of the locals by using a 10×10 grid. That is, the system of the disclosure can segment each of the patch areas by mathematically deriving longitudes and latitudes for each patch, such that each patch possesses a 13.4 feet×13.4 area.

For purposes of description, processing has been described herein as processing a “photo”. However, it should be appreciated that such processing described as performed on a “photo” can be performed on content described as a photograph, digital photograph, digital photo, picture, video, digital video, image, digital image, and/or other content described using similar terminology. In general, the processing of the disclosure can be utilized with content or digital content, including a video, as may be desired.

In an embodiment of the disclosure, the process of determining a popular spot can begin with geographic segmentation that starts with the identification of a known geographic area of interest that represents a “site”. For example, a “site” can be the area that encompasses the Statue of Liberty. In such example, smaller “spots” of uniquely identified areas can provide different vantage points within the site. Accordingly, a “bottom up” approach can be used in which spots are identified and such identified “spots” can be accumulated into a site. Further, a first site can be geographically positioned next to or adjacent to a second site.

In accordance with at least one embodiment of the disclosure, the processing can include a determination of a “relevant universe” of all stored digital photos, i.e. “available photos” that can be used in the processing of the disclosure. Stored digital photos can be tied to an area with a related longitude and latitude with such point contained within the area. A photo can include or be associated with metadata that represents the location at which the photo was taken. Such location metadata can be in the form of a point defined in a coordinate system. For example, the point can be the longitude-latitude (long-lat) at which the photo was taken. Parameters can be established for variables that can dictate whether a photo will or will not be included in the processing of the system, i.e. whether a photo will be an “active photo” or an “inactive photo”. The parameters can include the current (age of photo) and definition or protocol that can be used to determine the current age of the photo, location type(s), various minimum volumes, popularity rankings, affinity groups, user identification and credentials, and other attributes of a photo.

Such attributes can be adjustable or variable through user interface with the system. For example, a photo can be deemed relevant and included, as an active photo, if less than one year old as determined by the date that the photo was taken. Such parameters that control whether a photo is an active photo or an inactive photo, can be adjusted as desired. For example, with some spots, a photo might be relevant, and included as an active photo, if less than 10 years old. With other spots, a photo may only be an active photo if less than 5 years old, for example. Additionally, photos can be included in the processing of the system, as an active photo, dependent on an interrelationship of the photo with other photos. For example, a density of photos can be taken into consideration where the system performs processing to determine how many photos there are in a particular area. If a threshold number of photos in an area has been achieved, then all of such photos in the area can be included as an active photo. On the other hand, if a threshold number of photos in an area has not been achieved, then such photos may be deemed to be inactive photos. That is, illustratively, photos in an area that have not collectively achieved a predetermined density threshold can be maintained as inactive photos in a database. The photos can be maintained on a back end of the system for example. As more photos are added to the particular area, the density of photos is the particular area, such as a patch, will increase. Once the particular density threshold is attained in the area, the photos can be become active, i.e. by virtue that requisite density has been attained—and a patch is thus evolved into a spot, for example. Other variables or parameters can affect whether a particular photo is included in processing as an “active photo” or whether such photo is “inactive”.

Inclusion of a photo or photos as active can be dictated, by the processing of the system, dependent on whether there are a sufficient number of photos of a particular patch or other location type or combination thereof. Inclusion of a photo or photos as active can be dictated by attributes of a populated matrix of attributes or characteristics. For example, a “location type” of a photo can include types such as see, do, eat, drink, stay, shop or conceptual. Such types can be associated with particular spots to see, particular activities to engage in, particular restaurants to eat at, particular restaurants to drink at, or particular hotels to stay at. Additionally, the inclusion or non-inclusion of a photo (as an active photo) can depend on attributes of surrounding areas. For example, photos in the top 20% of “local” areas, out of all local areas in a particular area, may be included in the processing as active photos. Such inclusion can be controlled by the processing of the system.

A further processing component of the system of the disclosure can include establishment or generation of “virtual containers”. These virtual containers can provide placeholders for segregation and accumulation of photos. The virtual containers can correspond to and be defined by each of the areas described above—including remote, territory, sector, quadrant, local, and patch areas. In at least some embodiments of the disclosure, each of the photos can be segregated based on location of the photo vis-à-vis the particular area or areas in which such location (of the photo) falls within. Processing can be performed on an available photo to determine which area(s) or virtual container(s) the particular photo belongs in. In such processing, a photo can “cascade” down so as to be associated or tagged with the various virtual container(s) to which the photo belongs. More specifically, processing can be performed so as to associate or tag a photo with: a remote area that geographically bounds the location of the photo; a territory (within the tagged remote area) that bounds the location of the photo; a sector (within the tagged territory) that bounds location of the photo; a quadrant (within the tagged sector) that bounds location of the photo; a local (within the tagged quadrant) that bounds location of the photo; and a patch (within the tagged local) that bounds location of the photo.

A further processing component of the system of the disclosure can include an auto incremented and counting routine. For example, further photos can be added into a particular patch. As the photos are added in, a count associated with the particular patch can be automatically incremented. The patches can be then be ranked and processing performed based on such ranking. A table of counts, for each patch, and rankings of the patches can be maintained by the system. A table of counts and rankings can be maintained based on the number of photos in patches. Additionally, a table of counts and rankings can be maintained based on attributes or characteristics of photos in the patches. For example, a table of counts and rankings can be maintained based on how many photos in each “patch” relate to places to eat. For example, a table of counts and rankings can be maintained based on how many photos in each patch relate to events to see. The table of counts and rankings can be maintained in a database for access by the system and updated or overwritten in some periodic manner—or based on additional data that is input into the system.

The processing as described herein, including components of the processing, can be executed periodically or at predetermined time(s). For example processing as described herein may be performed daily, hourly, weekly or other desired frequency and may be limited to or vary by particular identified geographic areas. Processing can be performed when a new photo is uploaded into the system, such as when a new photo is input from a user. Processing can be performed upon request by a requesting, authenticated user over an established network. Processing can be performed when a new photo or batch of photos is uploaded into the system from a user, a database, or a third party server, for example.

Hereinafter, further aspects of the systems and methods of the invention will be described.

In accordance with at least one embodiment of the disclosed subject matter, processing performed by the system can include accessing a photo database, which has been populated by photos from users and other sources. The photo database can contain location data regarding the photos. The processing can include determining popularity of specific areas based on photos associated with each respective area. The processing can include determining popularity of specific areas—such as the number of photos in a “patch”. A patch that can be the smallest area demarcated by the processing of the system. An area, such as a patch, can include the relative strength of a preference provided by the user, positive or negative. Popularity of a particular area can be based on various attributes of one or more photos. Popularity can be based on the number of photos in a particular area or areas, such as in a patch. Popularity of an area can be based on attributes of a photo including location data associated with the photo, time data associated with the photo, and various other data associated or appended to the photo or to a cluster of photos.

The area of a “patch” has been described herein for purposes of illustration. For example, a “patch” can evolve into a “spot” if density of photos therein is sufficient. However, other areas can also be considered for and attain “spot” status, as described herein. For example, a geographic region such as a national state park might be processed to determine if such region possess sufficient density (of photos) such that the region should be deemed a spot.

Popularity of a particular area can also be based on “location type” and the number of photos in such area that are associated with such location type. Accordingly, a given area (which can be a “patch”) can be saved in the database (of the system) and tagged with a particular location type. In other words, the area can be associated with an attribute that indicates the area is of the particular location type. Such association or tagging can be performed utilizing a relational database, for example. Then, a photo may be associated with the area based on the location (of the photo) being located within the boundaries of such given area. Processing can then be performed to determine what “type” or “types” is the photo that was input. It may be the case that the photo is of a “type” that is the same as the “location type”. Accordingly, the input of such photo can contribute to a “location type count” or tally of how many photos of the particular “type” are in the area of the particular “location type”. In other words, if a photo in a particular area is of a type that corresponds to a “location type” of the area—then that photo will contribute to what might be referred to as a “location type count” of that area. Such “count” processing can thus provide popularity of a particular area with regard to the particular type. Such data can then be used to compare different areas, such as to compare different patches for comparative ranking.

It should be appreciated that a given area is not limited to one “location type”. Additionally, a given photo is not limited to be of one “type”. Accordingly, a particular area can be, i.e. can possess an attribute of, one or more location types. A particular photo can be, i.e. possess an attribute of, one or more types. For example, a photo taken at a popular restaurant at Niagara Falls can be tagged as “where to see” and “where to eat”. Relatedly, the “spot” in which such restaurant is located can be tagged as “where to see” and “where to eat”. As a result, the particular photo can contribute to the “location type count” of the spot for both “where to see” and “where to eat”.

In accordance with at least one embodiment of the disclosed subject matter, coding or instructions of the system can identify location types (of areas) and types (of photos) as may be desired. Location types that are available for association or tagging of an area can be different for different areas. For example, an area that has only one restaurant can be tagged with a more general “location type” that can include “where to eat”. On the other hand, another area can be densely populated with restaurants. Accordingly, the more general “location type” of “where to eat” can be further broken out into additional location types such as “Where to eat—American”, “Where to eat—Italian”, “Where to eat—Mexican”, and “Where to eat—fast food”.

For purposes of illustration, “location types” can include (1) “places” that can be organized by common characteristics such as consumer driven activities. Such “places” location type can be further differentiated to additional location types or levels, or what might be referred to as sub-levels. The further levels or sub-levels can include: a) where to see; b) where to photograph; c) activities to do; d) where to eat; e) where to drink beverages; f) where to stay, and g) where to shop, for example.

The location types can further include (2) “events” that can be tied to locations that may be activity driven, group attended (like parades or festivals) or newsworthy items that can occur more randomly.

The location types can further include (3) “things” that may include tangible items like candidates tied to a geographic area or intangible conceptual items like a referendum.

The location types can further include (4) “virtual” that may include user defined or “other” items assessed for popularity, user or voter preference.

As described above, the system can process geographic demarcations that can be referred to as “areas”. A particular type of area, i.e. the smallest type of area, can be a “patch”. Each patch can have an attribute of one or more “location types”. A patch can be deemed more popular as more photos are associated with either the patch in general or with a location type(s) of the patch. A patch can be deemed to possess sufficient density of photos, i.e. may be deemed to be popular enough, to be a spot. The more popular spots can be referred to as “top ranked spots”.

Popularity of an area/spot can be determined by photographic vote, where one or more users submit photos that yield popularity values. Popularity values for each of a number of characteristics of the area can be determined from the photos and associated clusters of photos. Data regarding each photo, clusters of photos, and various other data can be stored in a suitable database so as to perform processing as described herein. Accordingly, a user's photo can be the user's vote.

FIG. 1 is a diagram showing a photo ecosystem or ecosystem 10 in accordance with at least one embodiment of the disclosed subject matter. The ecosystem 10 can include a photo system 100; a plurality of user devices 20, 20′, 20″; and a third-party resource(s) 30. The various components of the ecosystem 10 can be connected and in communication with each other utilizing a suitable network 11. For example, the network 11 can be in the form of or include the Internet, a private network, and/or some other network. The network 11 can be composed of a plurality of discrete networks that communicate with each other and with the components illustrated in FIG. 1. It should be appreciated that the diagram of FIG. 1 is for purposes of illustration. The photo system 100 can be in the form of one or more servers or in the form of a distributed computer system. While three user devices are illustrated in FIG. 1, the ecosystem 10 can include many more user devices—and may well include thousands or millions of user devices. Each of such additional user devices can interact with photo system 100. Additionally, while one third-party resource 30 is illustrated, it should be appreciated that many third-party resources can be utilized and included in the ecosystem 10. Additional systems, servers, processors, and other processing assets and/or database assets can be included in the ecosystem 10.

The photo system 100 can perform various processing as described herein based on instructions stored in the database portion 120. The photo system 100 can store instructions so as to provide the processing described herein and can store the various photos, i.e. photo data that can include digital image data (of the image itself—a reproduction of what would be viewed by the human eye) as well as metadata about the photo, that is processed by the photo system 100. The photo system 100 can be connected to the network 11 so as to receive data from a variety of devices. The devices can be stationary in nature, like a desktop computer used for planning future location visits across the earth. The devices can be mobilized to include data identifying a current location and for establishing an area that is proximate to the user—and that is of immediate interest to the user. The photo system 100 can interface with the user device 20 so as to provide a variety of features to the user device 20. The photo system 100 can input data from the user device 20. The photo system 100 can output data to the user device 20.

The photo system 100 can include a computer processor (CP) 110 and a database portion 120.

The CP 110 can include a variety of processing portions as illustrated. Additionally, the database portion 120 can include a variety of database portions as illustrated.

The CP 110 can include a general processing portion 111. The general processing portion 111 can perform various general processing so as to perform general operations of the photo system 100. The general processing portion 111 can perform processing based on instructions contained in the database portion 120. The general processing portion 111 can perform any of the processing required or desired (so as to provide functionality of the photo system 100) that is not handled by the more specialized processing portions 112-116. However, it should be appreciated that the processing performed by the general processing portion 111 can be specialized in and of itself so as to provide the various functionality described in this disclosure.

The CP 110 includes the area segmentation processing portion 112. The area segmentation processing portion 112 can handle segmentation processing as described herein. Accordingly, the area segmentation processing portion 112 can handle segmentation of an area, for example the world, into first level areas, second level areas, third level areas and so forth. The area segmentation processing portion 112 can handle segmentation down to the level of a “patch”. The area segmentation processing portion 112 can handle various related processing.

The CP 110 also includes the photo input processing portion 113. The processing portion 113 can handle photo input processing as described herein. Such processing can include various processing related to the input of a photo, interfacing with a user in conjunction with input of a photo, processing that is performed once the photo is input, processing of metadata associated with the photo, and various related processing.

The CP 110 also includes the spot generation processing portion 114. The processing portion 114 can handle spot generation processing as described herein. Such processing can include generation of a “spot” once predetermined thresholds have been attained such that a particular area is to be identified as a spot, generation and saving of data in conjunction with generation of a spot, and various related processing.

The CP 110 can also include the user engagement processing portion 115. The processing portion 115 can handle user engagement processing as described herein. Such processing can include a wide variety of processing related to user engagement including using credentials to identify a current user, setting up a new user on the system, establishing preferences or settings of a user, and various related processing.

The CP 110 can also include the collective user processing portion 116. The processing portion 116 can handle collective user processing as described herein. Such processing can include various processing related to crowd sourced information, user review processing, user rating processing, user feedback processing, other processing that relates to interfacing with a plurality of users or other persons on an aggregated basis, and various related processing.

The photo system 100 can include the database portion 120. The database portion 120 can include a general database 121. The general database 121 can include various data used by and/or generated by the general processing portion 111.

The database portion 120 can include an area segmentation database 122. The area segmentation database 122 can include various data used by and/or generated by the area segmentation processing portion 112.

The database portion 120 can include a photo database 123. The photo database 123 can include various data used by and/or generated by the photo input processing portion 113.

The database portion 120 can include a spot generation database 124. The spot generation database 124 can include various data used by and/or generated by the spot generation processing portion 114.

The database portion 120 can include a user engagement database 125. The user engagement database 125 can include various data used by and/or generated by the user engagement processing portion 115.

The database portion 120 can include a collective user database 126. The collective user database 126 can include various data used by and/or generated by the collective user processing portion 116.

The photo system 100 can be in the form of or include one or more computer processors and one or more database portions 120. The photo system 100 can include or be in the form of a server. Various further details of the photo system 100 and the processing performed thereby are described below.

FIG. 2 is a high level flowchart illustrating processing that can be performed by the photo system 100, in accordance with principles of the disclosed subject matter. The processing can start in step 400 with initiating of photo processing. For example, step 400 can include enabling the system 100 or turning the photo system 100 “on”.

The processing of FIG. 2 can pass from step 400 onto step 401. Step 401 illustrates various processing that can be performed by the system 100. The processing illustrated in step 401 can be performed in serial or in parallel relative to each other and need not be performed in the particular order illustrated in FIG. 2. In a particular implementation of the system 100, some processes of FIG. 2 can be performed or enabled and other processes may not be performed or enabled.

With reference to step 401 of FIG. 2, in step 500, the system can perform area segmentation processing. Further details are described below with reference to FIG. 3. In step 520, the system 100 can perform photo input processing. Such processing can include the input of photos in real time or in batch manner, for example. Further details are described below with reference to FIG. 6. In step 560, the system 100 can perform spot generation processing. Further details are described below with reference to FIG. 9. In step 700, the system 100 can perform user engagement processing. Further details are described below with reference to FIG. 14. In step 810, the system 100 can perform specialty processing. In step 950, the system 100 can perform collective user processing wherein the system engages with a plurality or mass of users to perform affinity groups processing, user following processing, assessment of ratings view processing, and related processing. In step 820, the system 100 can perform general processing. The various processing performed in step 401 can be performed by one or more of the processing portions in the CP 110. Further details are described below.

FIG. 3 is a flowchart showing further details of the system 100 performs area segmentation processing, which can be called from the processing of FIG. 2, in accordance with principles of the disclosed subject matter. As illustrated, the processing starts in step 500 with segmentation of a predetermined geographic area, i.e., the world in this illustrative case. As reflected at 500′, in initial segmentation of the world, the world can be a current parent area and “remote areas” can be child areas. That is, the processing of FIG. 3 illustrates processing in which a top down approach is utilized. In such processing, an initial area to be segmented can be segmented at a first level, each of the first level segmentations can then be segmented at a second level, and so forth—so as to attain a desired segmentation. Alternatively, a bottom up approach can be utilized. In a bottom up approach, the lowest level area can be segmented, a predetermined number of the lowest level areas can then be aggregated together so as to form a next higher up level, areas in the next higher up level area can then be aggregated so as to perform a further next higher up level, and so forth. In such manner, a desired segmentation can be attained.

Accordingly, with bottom up segmentation processing, the system builds up from smallest child to largest parent. With top-down segmentation processing, the system builds down from largest parent to smallest child. In accordance with at least one embodiment of the disclosure, the largest parent can be the world, whereas the smallest child can be demarcated as a “patch” as described herein.

After the processing is initiated in step 500, the process passes onto step 501. In step 501, for the current parent area, the system can identify an anchor point for the first child area (or for the current child) and assign such anchor point as a current anchor point. The anchor point can be long-lat coordinates or other coordinates.

After step 501, the process passes onto step 502. In step 502, processing is performed to assign an area identifier and identify boundaries of the area. In such processing, subroutine 510 of FIG. 4 can be called. As reflected at 502′, the area identifier can be a unique identifier that can be a longitude-latitude point (long-lat) of a corner of the area. Accordingly, in one embodiment of the disclosure, one anchor point can identify location of the area and a height, width, or other geometrical extent can be used to identify boundaries of the particular area. In some embodiments of the disclosure, an upper left (in long-lat coordinates) and lower right (in long-lat coordinates) of the area can be used so as to define the area. With either approach, a unique identifier can also be assigned to each area so as to be used in processing of the area. After step 502, processing passes onto step 503.

In step 503, the process determines if the current area, which can be referred to as a parent area, can be segmented into a further child (i.e. in addition to the children that have already been formed out of the parent area). Such processing component is indicative of a top down approach, in contrast to a bottom up approach. In other words, the decision processing of step 503 determines if the current area has been fully segmented out such that no further segmentation is needed (in order to segment the current area). As reflected at 503′, such determination processing of step 503 can be based on whether a boundary of the current area coincides with an anchor point of a previously processed area. If a boundary of the current area does coincide with an anchor point, such can indicate that the processing has reached the end or limit of the current area. In some embodiments, the processing can advance in a horizontal manner—to segment across an area—until a boundary is reached. Then, the processing can start a new “row” below the row that was just segmented. In such manner, for a given area, the processing can advance across and drop down a row; across and drop down a row; across and drop down a row; and so forth until the particular area has been fully segmented. However, other methodologies can be used.

With further reference to step 503 of FIG. 3, if the decision of step 503 is yes, then the processing passes onto step 504. In step 504, based on an outer most boundary of the current child area, the process can assign a point on such boundary as a new “current anchor point”. More specifically, the process may assign the upper point on a right boundary line as the new “current anchor point”. If the upper point on the right boundary line has already been assigned to be an anchor point, or is positioned outside the current area being processed—then the system can know that the particular row is completed. Upon such determination, the process can “drop down” a row so as to segment the next row.

Once the new current anchor point is identified/determined in step 504, the processing passes back to step 502. In step 502, processing continues as described above.

On the other hand, it may be determined in step 503, that the current parent area cannot be segmented so as to form a further child. In other words, a no determination in step 503 indicates that the current parent area has been fully segmented into child areas. As a result, the process passes from step 503 onto step 505.

In step 505, the processing determines if there are more parent areas (at the current level) to segment. If yes, then the process passes onto step 506.

In step 506, the next parent area to segment is assigned to be the current parent area. The process passes from step 506 back to step 501. Processing then continues as described above. On the other hand, it may be determined that there are not more parent areas (at the current level) to segment. Such no determination in step 505 indicates that all the areas at the current level have been segmented out, i.e. such that children of the current parent have been created. Accordingly, the process passes from step 505 onto step 507.

In step 507, the processing determines if segmentation should be performed down a further level. If yes in step 507, the processing passes onto step 508. In step 508, the process advances to the next lower level. Accordingly, the first child area (of the children areas just created) becomes the current parent area. Also, the level of the new parents is assigned to be the current level. More generally, as reflected at 508′, the newly created children now become the parents. The processing passes back to step 501. In step 501, the processing continues as described above.

It may be determined in step 507, that segmentation is not to be performed down a further level, i.e. that the segmentation processing has indeed attained the lowest level to be segmented. Such lowest level can be the “patch” level as described herein. As reflected at 507′, a no determination in step 507 reflects that all of the segmentation areas now have unique identifiers and that all boundaries of the areas have been formed. Accordingly, the process passes from step 507 onto step 509. In step 509, the system has completed the segmentation processing. Accordingly, the process returns to FIG. 2.

FIG. 4 is a flowchart showing details of “processing is performed to assign area identifier and identify boundaries of the area” of subroutine 510 as called from FIG. 3, in accordance with at least one embodiment of the disclosed subject matter. As shown, the process starts in step 510 and passes onto step 511. In step 511, the system assigns a unique area identifier to the current anchor point. In one embodiment, the unique area identifier can correspond to one corner of the area using longitude and latitude values. Then, the process passes onto step 512.

In step 512, the system can retrieve current X, Y advance parameters for the current level. The current advance parameters can dictate magnitude of a new area to be formed, or in other words to be segmented out. If the X, Y advance parameters are 10 miles, 10 miles, respectively—then an area that is 10 miles wide and 10 miles high will be created. Such X, Y advance parameters can be utilized to create the segmentation areas described above. Such segmentation areas can include remote, territory, sector, quadrants, local, and patch. Accordingly, it should be appreciated that as the system performs segmentation processing, the system can retrieve the particular X, Y advance parameters that correspond to the current level being processed. The X, Y advance parameters can be selected so as to evenly segment a current parent area into children areas. In at least some embodiments, it may be the case that all the children areas are not of the same magnitude in square miles or in square feet, for example. Additionally, the advance parameters can be more complex than X, Y advance parameters. More complex advance parameters can be used when segmenting more complex geographical areas, such as the circular curvature of the world or globe.

After step 512, the process passes onto step 513. In step 513, based on the advance parameters, the system identifies corner points and/or boundaries of the current child area. As a result, as reflected at 513′, the CP 110 has now created a new current area.

After step 513, the process passes onto step 514. In step 514, the process returns to FIG. 3. Specifically, the process passes onto step 503 of FIG. 3.

FIG. 5 is a diagram showing segmentation of a remote segmentation area 150. In the example, a “remote” area is the current parent area and “territory” children areas are being formed/segmented out of such parent area 150. As shown in FIG. 5, segmentation of the area 150 (current parent area) into children areas is almost complete, i.e. with five children areas to be formed or further segmented out. The current child area 160 has been assigned an area ID (identification) of T096. Such area T096 possesses an anchor point 161. In this example, the anchor point 161 can be an upper left hand corner (in long-lat coordinates) of the area T096. The area T096 can also include a boundary point 162. The boundary point 162 can be provided at the upper right-hand corner of the area T096. The boundary point 162 may have been identified by the current X, Y advance parameters as described above with reference to step 512 of FIG. 4. Once the territory area 160 is saved into memory, the processing can then use the boundary point 162 as the next anchor point—for the next territory area to be segmented out. Such is illustrative of the processing of step 504 of FIG. 3. The area 160 can be defined based on a predetermined height (as shown in FIG. 5) so as to define the two-dimensional area. The area can be defined so as to be square—and thus the distance between the points 161 and 162 can also serve to define the height (as shown in FIG. 5) of the two-dimensional area 160, as well as the height.

As illustrated in FIG. 5, after the formation of the area 160, the system has five more territory levels to form (in the remote area 150) in order to fully segment out the remote area 150. The system, i.e. the CP 110, can determine that segmentation is complete once (1) an upper right boundary point 162 of an area corresponds to a right hand boundary of the area 150 and (2) a lower right boundary 162 corresponds to a lower boundary of the area 150, for example.

Once segmentation of the current parent area 150 is completed, then the processing can advance to the next parent area (at the current level). That is, the processing can advance from the remote area 150 onto the remote area 151.

FIG. 6 is a flowchart showing details of the “photo system (PS) 100 performs photo input processing” of step 520 of FIG. 2, in accordance with principles of the disclosed subject matter. Such processing can include the inputting of a photo or photos with either batch or real time processing, for example. As shown, the process starts in step 520 and passes onto step 521.

As reflected at 520′, a “photo” or “photo data” can include both image data (that represents a reproduction of the image that was viewable by the human eye) and various metadata (that contains data about the photo, such as date/time that the photo was taken and location of the photo). The location data of a photo can be in the form or include a point or geographical point. For example, the point can be the longitude-latitude (long-lat) at which the photo was taken.

In step 521 of FIG. 6, the system determines that one or more photos are in queue for processing of the one or more photos by the system 100. Accordingly, the processing of step 521 assumes that one or more photos are in queue for processing. After step 521, the process passes onto step 522.

In step 522, the system determines if the one or more photos are aggregated (in queue) in a batch manner. In other words, processing can determine if a group of photos has been uploaded to the system in a batch manner. In such situation, it may be desirable or beneficial to capture the fact that such photos were input together in a batch manner. In other words, it may be beneficial to capture such interrelationship between such uploaded photos. The processing of step 524 provides such capture of interrelationship between the photos. That is, if yes in step 522, the process then passes onto step 524. In step 524, for each photo, the system assigns both a photo ID and a batch ID. The batch ID can be common to all photos in the particular batch.

Accordingly, the interrelationship or association between the photos in the batch can be captured in the database portion 120. As shown at 524′, the batch ID can be used to perform “commonality processing” for the photos in the batch. After step 524, the processing passes onto step 525.

On the other hand, it may be the case in step 522 that the photos are not aggregated in a batch manner or that there is only one photo in queue for processing. As a result, the process passes from step 522 onto step 523. In step 523, for each photo, the system assigns a photo ID. The process then passes onto step 525.

In step 525, the first photo to be processed is assigned to be the current photo. Then, in step 530, the system processes the current photo so as to integrate such photo into the system. Such processing can include integration into the database and photo inventory of the system.

Subroutine 540, of FIG. 7, can be called upon so as to process each photo. Then, after step 530, the processing passes onto step 531. In step 531, a determination is performed by the system of whether there is another photo to be processed. If yes, then the processing passes onto step 532.

In step 532, the system retrieves the next photo and assigns such retrieved photo to be the “current photo” in the processing. Processing then passes back to step 530. Processing then continues as described above.

Alternatively, it may be determined in step 531, that there is not another photo to be processed. Accordingly, a no determination is determined in step 531. As shown at 533, such reflects that all photo or photos that were input by the system have been processed. With such determination, the processing passes from step 531 onto step 534. In step 534, photo input processing is terminated for the particular photo(s) or for the particular batch of photos.

FIG. 7 is a flowchart showing further details of the system “processes current photo so as to integrate such photo into the system” of subroutine 540 as called from FIG. 6, in accordance with at least one embodiment of the disclosed subject matter. As shown, the process starts in step 540 and passes onto step 541. In step 541, the system determines, based on metadata of the photo, does the photo possess metadata that satisfies predetermined criteria. For example, the processing of step 541 can relate to determining whether the metadata contains appropriate location data that can include longitude and latitude data, appropriate date and time data, data indicating a source of the image such as a particular user, and other requisite data. If no in step 541, the process passes onto step 544. In step 544, the photo can be placed into an archive database or other suitable database and will not be placed in a virtual container, in at least one embodiment of the disclosure. In other words, the photo will not be placed into the active inventory of the system. A communication can be sent to the originating user indicating that the metadata associated with the photo was insufficient to be included in the photo system.

If a yes determination is determined in step 541, then the process passes onto step 542. In step 542, based on metadata of the photo, the system determines whether the photo possesses data to satisfy predetermined verification requirements. For example, was appropriate biometric data included with the photo for verification of the photo, were other security protocols satisfied, and/or was an appropriate IP address of a source user device received. If no, than the processing passes to step 544. In step 544, processing is performed as described above.

If yes in step 542, the process passes onto step 543. In step 543, the processing can determine, based on the metadata of the photo, does the photo satisfy any other applied constraints. If no, then the processing again passes to step 544.

On the other hand, if yes in step 543, then the process passes onto step 545. In step 545, the photo is tagged as satisfying all required criteria for placement into a virtual container or in other words for the photo to be placed in the active inventory of the system as an active photo. As a result, a communication can be sent to the user. Such communication can be of a congratulatory nature indicating that his or her input photo has been successfully input into the photo system 100. Then, the processing passes onto step 546.

In step 546, processing is performed to place the photo into a virtual container. Subroutine 550 can be invoked to process the photo, as shown in FIG. 8. As shown in FIG. 7 and reflected at 546′, a particular area, such as the world or planet, can be first segregated into one of the approximately 19,700 remote areas. The input photos can cascade down and accumulate in accordance with established parameters to the lower levels of territories, sectors, quadrants, locals, and ultimately patches.

FIG. 8 is a flowchart showing in further detail subroutine 550 (called from the processing of FIG. 7) that can include processing to place the photo into a virtual container, in accordance with at least one embodiment of the disclosure. As is shown at 550′, the processing of FIG. 8 can include various components. The processing can accumulate photos through a cascading process to segregate photos geographically into virtual containers that cascade through 6 levels of refinement based upon volume relevance. The processing can count the photos within each of the virtual containers and test for volume relevance. In other words, the processing can determine volume relevance based on a count of photos in a virtual container.

The processing of FIG. 8 starts in step 550 and passes onto step 551. In step 551, location data of the photo is retrieved from the appropriate database. Then, in step 552, the process identifies the particular level-1 area to which the location data is associated and associates the photo to the identified level-1 area. The level-1 area can be one of the “remote” areas as described herein. As reflected at 552′, the area to which the particular photo is associated can be the area in which the longitude and latitude coordinates of the photo are bounded. In other words, the area to which the particular photo is associated can be the area in which the photo is located. Then, the process passes onto step 553.

In step 553, in the identified level-1 area, which was identified in step 552, the processing determines the level-2 area to which the photo is associated. The processing then associates the photo to the identified level-2 area. Such identified area is then allocated a count.

In step 554, in the identified level-2 area, which was identified in step 553, the processing determines the level-3 area to which the data is associated. The processing then associates the photo to the identified level-3 area. Such identified area is then allocated a count.

In step 555, in the identified level-3 area, which was identified in step 554, the processing determines the level-4 area to which the photo is associated. The processing then associates the photo to the identified level-4 area. Such identified area is then allocated a count.

In step 556, in the identified level-4 area, which was identified in step 555, the processing determines the level-5 area to which the photo is associated. The processing then associates the photo to the identified level-5 area. Such identified area is then allocated a count.

In step 557, in the identified level-5 area, which was identified in step 556, the processing determines the level-6 area to which the photo is associated. The processing then associates the photo to the identified level-6 area. Such identified area is then allocated a count. The level 6 area can be a patch or patch area.

Accordingly, as shown in FIG. 8, cascade processing can be utilized so as to associate an input photo into a respective virtual container for each of the levels. After step 557 of FIG. 8, the processing passes onto step 558. In step 558, photo placement processing is completed for the particular photo. As reflected at 558′, the processed photo, as well as photos that have been previously processed are now accumulated within the various virtual containers. Such photos can be in the many to thousands or millions.

Each “area”, as described herein, can constitute or include a “virtual container” and/or be represented by a virtual container. Accordingly, for example, each of the “patches” as described herein can constitute a virtual container.

In step 558, the processing can pass back to step 531 of FIG. 6. Processing can then continue as described above.

FIG. 9 is a flowchart showing further details of the “system performs spot generation processing” subroutine 560 of FIG. 2, in accordance with principles of the disclosed subject matter.

As described above, a “patch” can be a smallest area of the various areas that are segmented out. A “patch” can be approximately 13×13 feet, for example. In “spot” generation processing, a “patch” can be elevated to a “spot”—depending on attributes of the particular patch. Such attributes can include the density of photos in the particular patch. If the density of photos surpasses a predetermined threshold, the “patch” can be elevated to the stature of a “spot”. Once elevated, such spot can be subject to various processing, such as being identified in search results and/or be given a higher ranking or rating.

As shown in the processing of FIG. 9, the process starts in step 560 and passes onto step 561. In step 561, the system watches for an event to be observed that invokes spot generation processing. In performing such processing of step 561, subroutine 570 can be called upon or invoked. Further details are described with reference to FIG. 10 below.

Relatedly, as is shown at 561′, the process of step 561 can be performed at various times. For example, the processing of step 561 can be performed daily, hourly, weekly, or at other desired frequency and may be limited to or vary by particular identified geographic area. The processing of step 561 can be performed when a new photo is uploaded into the system. The processing of step 561 can be performed upon request by a requesting user over an established network. Further details are described below with reference to FIG. 10.

Based upon the system watching for an event in step 561, in step 563′, the system can perform a determination of whether an event was indeed observed. If no in step 563′, the system continues to watch for an event as reflected at 562. Accordingly, the processing loops back to step 561 and continues as described above.

On the other hand, if yes in step 563′, the process passes onto step 563. In step 563, if the event, which was identified in step 561, included an input photo—then a subroutine can be invoked to process the photo. Specifically, the subroutine 540 of FIG. 7 can be invoked to process the photo. Then, the process passes onto step 564. In step 564, based on attributes associated with the event, the geographic region upon which to perform spot generation is determined. Then, the process passes onto step 565.

In step 565, for the identified geographic region, spots in such region are generated based on photo content in such geographic region. In other words, patch areas in the identified geographic region can be evolved to be spots. Subroutine 600 can be called as shown in further detail in FIG. 11 and described below.

After step 565, the process passes onto step 566. In step 566, the system generates a communication that patches in the particular region have been involved to spots. For example, a communication can be generated and output to a requesting user or to a user that submitted a photo that contributed, in some particular way, to the promotion of a patch to spot. Then, the process passes onto step 567. Step 567 reflects that the processing has been completed. In other words, the processing of the subroutine 560, as shown in FIG. 9, is completed. It should be appreciated that various other processing and subroutines as shown in FIG. 2 can be continued or further called upon as otherwise described herein.

As reflected at 561″, the described processing relates to “patches”. However, similar processing can be applied to any virtual containers of a level, such as “local” (level-5) or “quadrants” (level-6), for example. A “local” area that has evolved into a spot can be described as a “local-spot”.

FIG. 10 is a flowchart showing in further detail the “system watches for an event to be observed that invokes spot generation processing” subroutine 570, as called from FIG. 9. The processing of step 570 of FIG. 10 can include various components 571, 572, 573, 574, and 575. These various processing components can be performed in parallel using respective subroutines that can be performed by the system 100. In step 571, the system can determine if a time marker has been attained based on some predetermined periodicity. In step 572, the system can determine if a new photo has been loaded into the system. In step 573, the system can determine if a threshold number of photos have been loaded into the system. In step 574, the system can determine if a threshold number of photos of a particular type or attribute have been loaded into the system. In step 575, the system can determine if a user or administrator has requested spot generation processing.

Accordingly, the system can determine if various “triggers” of steps 571, 572, 573, 574, and 575 have been satisfied—so as to enable or activate the processing of each of such steps. Enablement (i.e. whether the processing of such steps is available) of any of such steps 571, 572, 573, 574, and 575 can be performed through suitable settings, which can be controlled by an administrator or user. Additionally, thresholds, parameters, or other attributes of any of the steps 571, 572, 573, 574, and 575 can be adjusted by an administrator or user as may be desired. It should be appreciated that processing of some of the steps 571, 572, 573, 574, and 575 may be enabled, whereas other steps are not enabled.

With further reference to FIG. 10, if a yes determination is determined in any of the various steps 571, 572, 573, 574, and 575, the processing can pass onto step 576 of FIG. 10. In step 576, based on the particular request, the system can attach control data to the processing request. This control data can control processing of the request. For example, the request can be limited to a particular geographic area, such that only “patches” in such geographic area are processed for possible elevation to spot status.

After step 576, the process passes onto step 577. In step 577, the processing passes back to FIG. 9—and specifically passes to step 563′.

FIG. 11 is a flowchart showing further details of a “spot generation processing” subroutine 600, as called from FIG. 9, in accordance with principles of the disclosed subject matter.

More specifically, the processing of FIG. 11 relates to the consideration of patches, in a predetermined geographical region, that may be promoted to the status of spots. The patches in the particular geographic region can be considered for promotion based on photo content in such geographic region.

The processing of FIG. 11 starts in step 600 and passes to step 601. In step 601, the system determines the various “patches” that are in the identified geographic region (IGR) AND that have at least one (1) count, i.e. at least one photo associated with the patch, in accordance with at least one embodiment of the disclosed subject matter. As described above, a patch can be in the form of or include a virtual container in which photos are placed (or associated with). An identified geographic region may be all the patches in a particular “local”; all the patches around a particular point of interest; all the patches in a particular geographic region, such as along a coast line; or all patches that have some other common attribute or that are identified for processing and some suitable manner. Additionally or alternatively, patches can be processed in some sequential manner. Accordingly, the processing of step 601 contemplates that not all patches in the world or country, for example, will be processed at the same time. Rather patches can be carved out and processed in groups and/or processed sequentially dependent on processing capabilities and bandwidth that is available.

After the processing identifies the patches to processed in step 601, the processing passes onto step 602. In step 602, the system identifies the first patch that is in the IGR and tags such as the current patch. Such tagging can identify the particular patch as being the next patch to be processed. After step 602, the process passes onto step 603.

In step 603, the system retrieves data, including photo count, that is associated with the current patch. In other words, how many photos have been associated with the particular patch. Then, the process passes onto step 604.

In step 604, the system determines, for the current patch, if the number of photos contained therein exceed a threshold. For example, the threshold could be 20 photos that have been associated with the current patch. If 20 photos have not been associated with the current patch, then a no is rendered in the processing of step 604. As a result, the process passes from step 604 onto step 607.

On the other hand, a yes determination may be rendered in the processing of step 604. Such yes determination reflects that the current patch has indeed attained 20 photos associated therewith. Based on the yes determination in step 604, the process passes onto step 605. In step 605, the current patch is tagged (by the system) to constitute a “spot”. In accordance with at least one embodiment of the disclosure, a patch designated as a spot will then be rendered in search results, as further described below. On the other hand, a patch that has not been involved to be a spot may not be rendered in search results.

After step 605 of FIG. 11, the process passes onto step 606. In step 606, the system performs “type tagging” for the current patch, which has now attained spot status. Such elevated patches can be deemed a “patch-spot”. To perform the processing of step 606, subroutine 610 can be called as described below with reference to FIG. 12. The type tagging of step 606 can also be performed for patches that have not attained the predetermined threshold (step 604).

After step 606, the process passes onto step 607.

In step 607, the system identifies the next patch, if any, that is in the identified geographic region (IGR) and tags such next patch as the current patch. As reflected in the processing of step 607′, the system may determine that there is not a next patch. As a result, the process passes onto step 609. In step 609, the processing passes back to FIG. 9—and specifically passes onto step 566 of FIG. 9.

With further reference to FIG. 11, and in the situation that there is indeed a further patch or next patch identified in step 607, the processing passes from step 607 back to step 603. In step 603, the system retrieves data, including photo count, that is associated with the current patch. Processing then continues as described above.

As shown at 600′ (FIG. 11), a “spot” status can reflect an elevated state or status of any of the 6 types of areas, and not just a “patch”. Relatedly, the processing of FIG. 11, and in particular step 604, is based on whether a particular photo count has been attained by a particular patch. However, the systems and methods are not limited to such particulars. For example, a “spot” can be a local or other area that has been elevated to be a “spot” due to (a) number of photos in the particular area, (b) same types of photos in the particular area that exceed some threshold, and/or (c) based on other popularity rankings, attributes, etc. that have been attained in the particular area.

In accordance with principles of the disclosed subject matter, the location of a photo can be a point, i.e. a longitude/latitude point (long/lat point). The area to which the photo is to be associated can be determined mathematically—by determining the particular area in which the photo is bounded. Relatedly, there may by a case in which an area, such as a patch, is not fully encompassed with an identified geographic region (IGR). For example, an area might be generated to be around a landmark or an area might be drawn or designated by a user. Such area might be split or cross-over two or more IGRs. In such a situation, settings may be provided to control what constitutes “in” an identified geographic region, e.g. (a) fully encompassed within, or (b) partially within. Thus, for example, if a particular patch is only partially in an IGR to be processed (step 601 of FIG. 11), then such patch may indeed be processed based on an inclusive setting. However, it may be that another setting dictates that a particular patch has to be fully within an IGR. Other parameters and/or attributes of a patch (or other area) and/or a particular IGR can be utilized so as to dictate whether a patch will be processed or will not be processed in a particular IGR.

FIG. 12 is a flowchart showing in further detail the system performs “type tagging” for the current patch of subroutine 610 of FIG. 11, in accordance with principles of the disclosed subject matter.

The process begins in step 610 and passes onto step 611. In step 611, the system performs “type tagging” for the current patch for a type-A photo. The processing of step 611 can call upon the subroutine 620 of FIG. 13. Then, the process passes onto step 612.

In step 612, the system performs “type tagging” for the current spot for a type-B photo. In step 614, the system performs “type tagging” for the current spot for a type-Z photo. As reflected at 613 of FIG. 12, it should be appreciated that processing can be performed for any desired number of “types” of photos. For example, as shown at 613′, additional photo types can include a variety of location types.

For purposes of illustration, subroutine 620 of FIG. 13 shows type tagging to determine whether a particular spot has one or more type-A photos. However, the processing as illustrated in FIG. 13 can be applied to any of the types illustrated in FIG. 12 or other types, as may be desired.

Accordingly, in the various processing of FIG. 12, the system determines what type of photos are associated with the particular spot. As described above, a particular one photo can be tagged as two or more types. Thus, for example, a particular photo might be tagged in both step 611 and step 612 of FIG. 12.

After all the types have been processed in FIG. 12, the process passes onto step 615. In step 615, the processing returns to FIG. 11—and specifically passes onto step 607 of FIG. 11.

FIG. 13 is a flowchart showing further details of the system performs “type tagging” for the current spot for a type-A photo of subroutine 620, in accordance with at least one embodiment of the disclosed subject matter. As shown, the process starts in step 620 and passes onto step 621.

In step 621, the system determines, for the current spot, if any photos contained therein are tagged as a type-A photo. For example, a type-A photo might be a “parade event”, for example, as reflected at 621′. However, it is appreciated that a photo can be possess or be attributed with any of a variety of types. Such “type” can include any “thing” or attribute that is associated with the particular photo. For example, the “thing” that is associated with the photo might be a particular time window in which the photo was taken.

If yes in step 621, the process passes onto step 622. In step 622, the system determines the number of photos that are tagged as type-A. Then, in step 623, the system associates data (from step 622) with the particular spot so as to be searchable by a user. Then, the processing passes onto step 625. In step 625, the processing returns to FIG. 12 and step 612. In step 612, the processing continues as described above—to process the current spot to identify further types of photos in such spot.

On the other hand, a no determination may be rendered in step 621. Accordingly, the processing passes from step 621 onto step 624. In step 624, the system has determined that the current spot does not possess any type-A photos. The system can then store such determination. Then, processing passes onto step 625. Processing then continues as described above.

As reflected at 623′ of FIG. 13, a searching user may enter search criteria of type-A in a certain number. For example, a user may search for a particular area, i.e. a particular geographical area, that has at least 10 type-A photos. Additionally, search criteria may not be constrained or dictated by a certain number of a particular type of photo in a spot. Rather, search criteria can utilize top ranked spots or other areas. In such processing, the top ranked spot or spots can be returned as search results regardless of the particular number of a particular type of photo associated with such spot.

FIG. 14 is a flowchart showing in further detail the subroutine 700, system performs user engagement processing, invoked or called upon from FIG. 2. The processing of subroutine 700 can include both step 701 and step 702, in accordance with embodiments of the disclosed subject matter. In particular, the processing of FIG. 14 relates to interfacing with a user device, which is associated with a human user, to input both a new photo that can include image data and attributes or metadata regarding such new photo. The processing of steps 701 and 702 can be performed in parallel or in serial and need not be performed in the particular manner illustrated in FIG. 14.

In step 701 of FIG. 14, the system interfaces with a user device to input a new photo. In such processing, subroutine 710 can be called upon or invoked. Such subroutine 710 is illustrated in further detail below with reference to FIG. 15. As reflected at 701 of FIG. 14, the system 100 can also interface with other systems to input photos in batch or serial manner, for example.

In step 702 of FIG. 14, the system interfaces with a user device to input attributes of the photo. In such processing, subroutine 730 can be called upon or invoked. Such subroutine 730 is illustrated in further detail below with reference to FIG. 16.

In step 760 of FIG. 14, the PS interfaces with the user device, i.e. the user, to perform photo user processing. In performing such processing, a subroutine 760 can be called or invoked. Such subroutine 760 is illustratively shown in FIG. 17.

FIG. 15 is a flowchart showing in further detail the photo system “interfaces with a user device to input a new photo” of subroutine 710 that can be called upon or invoked from the processing of FIG. 14. As shown, the process starts in step 710 and passes onto step 711. In step 711, the system waits for a request from a user device to input a photo. If no is rendered, then the process passes onto step 712. In step 712, the system continues to wait for user input.

On the other hand, a yes determination may be rendered in the processing of step 711, indicating that the system has indeed received a request from a user device to input a photo. Accordingly, the process passes onto step 713. In step 713, the system confirms identity of the particular user by inputting credentials from the user, confirming that credentials have already been input from the user, and/or authenticating the user device in some manner. Any suitable authentication mechanism, arrangement, or technology can be utilized so as to allow the system to confirm identity of the user device and/or human user. For example, biometrics can be utilized so as to authenticate the user device and/or human user. After step 713, the process passes onto step 714.

In step 714, the system confirms that the photo includes and/or is associated with metadata identifying the user and/or user device that took the photo. Then, the process passes onto step 715.

In step 715, the system confirms that the photo includes and/or is associated with metadata representing date and time that the photo was taken. Then, in step 716, the system confirms that the photo includes and/or is associated with metadata representing location that the photo was taken. After step 716, the process passes onto step 720.

In step 720, the system determines whether or not all requirements have been satisfied so as to input the photo into the system. If no, then the process passes onto step 723. In step 723, the system outputs a communication to the user that the photo, which the user submitted, is not accepted. Such communication can provide basis for not accepting the photo, so as to be helpful to the user.

If the processing determines that all requirements have been satisfied to input the photo into the system, in step 720 of FIG. 15, then a yes is rendered in step 720. Accordingly, the processing passes onto step 721. In step 721, the system interfaces with the user to input attributes of the photo. In such processing, subroutine 730 of FIG. 16 can be called upon or invoked.

Accordingly, it should be appreciated that the system can interface with a user so as to input attributes of the photo subsequent to input of the photo (step 721 of FIG. 15) or independently of input of the photo (step 702 of FIG. 14), i.e. at some later time relative to input of the photo itself.

After step 721 of FIG. 15, the process passes onto step 722. In step 722, the system places the photo, with metadata, in queue for processing. Accordingly, with the processing of step 722, the processing of FIG. 15 can be terminated. Once photo(s) are identified or observed as being in queue, the system can invoke or call upon the processing of step 521 of FIG. 6.

Accordingly, in the processing of steps 713, 714, 750, 716, various steps can be performed so as to determine if the photo possesses required attributes or metadata so as to be processed by the system 100. As reflected at 621′ of FIG. 15, such processing can be complementary to the processing performed in FIG. 6.

FIG. 16 is a flowchart showing in further detail the “PS interfaces with a user to input attributes of photo” subroutine 730 as called from the processing of FIG. 15. The process of FIG. 16 is initiated and the processing passes to step 731. In step 731, the PS waits for a request from the user via either of step 740 or step 750. In step 740, the PS monitors for an input request from the user to associate “location type” with the photo. In step 750, the PS monitors for an input request from the user to perform site association of the photo. For example, the “wait” processing of FIG. 16 can include the PS waiting for a user to log on or sign into the system, i.e. the PS (photo system) —and engage with the PS so as to render a yes in step 740 or step 750.

In the processing of step 731, as reflected at 731′, the PS can interface with the user to associate other or additional data with the particular photo that is being processed. Such data, which is then associated with the photo, can then be used in various processing. For example, the additional data associated or appended to the photo can be used by the PS to perform searching based on a user query, i.e. based on a user search. The additional data can be used to determine if a patch can be elevated to the disposition of a spot.

With reference to step 740, upon a request being received in step 740 such that a “yes” is rendered in the processing, the process passes to step 741. In step 741, the PS presents options to the user so that the user can select a particular location type, for example. That is, in the processing of step 741, the user can associate a photo with a location type. For example, the PS can interface with the user so as to present a photo to the user. The user might select the photo in some suitable way such as from an index of photos, a listing of photos, or in some other manner. Once a particular photo is selected, the user may be presented with a list of possible location types which may be associated with the particular photo. For example, “location types” that are presented to the user (as an option to associate with a photo) can include places, events, things, or virtual. Other location types can be provided as may be desired. The location type “places” can provide the user the ability to associate a photo with a particular place. The location type “events” can provide the user the ability to associate a photo with a particular event. The location type “things” can provide the user the ability to associate a photo with a particular thing. The location type “virtual” can provide the user the ability to associate a photo with a virtual concept, such as to provide an association of a photo with a game based event, for example.

With reference to step 750, the CP can determine that a request was indeed received from a user to perform site association of a photo. Accordingly, a yes is rendered in step 750. The processing then passes to step 751. In step 751, the PS retrieves location of the photo. For example, the PS may retrieve the location of the photo from metadata associated with the photo. Then, the process passes onto step 752. In step 752, the PS identifies sites that are associated with the location of the photo, i.e. the location that was retrieved in step 751. Then, the process passes onto step 753. In step 753, the PS associates the photo with the identified sites. For example, one “site” might be New York City. Another “site” might be Times Square.

Accordingly, a photo taken in Times Square can be associated (in step 753) with both the Times Square site and the New York City site. As reflected in step 753, popularity of the sites will be increased by the addition of the photo to that site, in accordance with at least some embodiments. As reflected at 754 of FIG. 16, the virtual container that the photo is associated with can be performed through separate processing vis-à-vis processing of step 751-753. Relatedly, a site can be composed of a plurality of spots.

FIG. 17 is a flowchart showing in further detail the “PS interfaces with a user device to perform photo user processing subroutine 760 of FIG. 14. The process is initiated and passes to step 761.

In step 761, various processing can be performed in step 762, 763, 764, and 765. Such processing can be requested or invoked through user interface.

In step 763 of FIG. 17, the PS can interface with the user to perform requested processing for the user. For example, the PS can interface with the user to perform “spots around me” or “spots near me” processing for the user. Such processing can also be described as “Spotz Around Me” processing. The term “spotz” can be used interchangeably with the term “spots” so as to reflect the novel processing of the disclosure. Additionally, the term “ShotSpotz” can be used to mean, be a part of, or include the photo system 100, in accordance with at least one embodiment of the disclosed subject matter. In such processing of FIG. 17, the PS can retrieve or input the geographical location of the user device so as to identify spots that are proximate to the user.

The proximity can be determined based on predetermined thresholds. That is, settings can be provided that can control whether “proximate” means within 1 mile of the user, 10 miles of the user, or 100 miles of the user, for example. The processing of step 763 can also include “find a spot” processing. In find a spot processing, the user can enter search criteria or search terms that contain criteria of what the user is looking for either in a photo or in a spot. For example, a GUI 1800 as shown in FIG. 18 can be utilized so as to input search criteria from the user. In regard to “find a spot” processing—such processing can include or be associated with planning, planning a trip, organizing a plurality of spots, saving a collection of spots, retrieving one or more spots, and related processing and functionality. Additionally, the processing of step 763 can include “upload a photo” processing. In such processing, a photo can be uploaded from a user device of the user. The photo can then be processed as otherwise described herein. Additionally, step 763 can include the option “use the camera”. With such option, the camera of the user device can be activated. FIG. 20 illustrates a GUI 2000 that can be utilized so as to launch or invoke the processing of step 763 (FIG. 17).

In step 762 of FIG. 17, the PS can interface with the user to input search filters to apply in performing search for photos. Such search filters can include such items as usage, lens, time of day, season, and crowd size. The processing of step 762 can provide functionality to allow a user to specify the things that the user wants to see, where to drink e.g., provide a filter for image types, filter an image based on metadata or attributes associated with the image, and/or provide other functionality. FIG. 21 illustrates a GUI 2100 that can be utilized so as to interface with the user so as to input selection of search filters. For example, a particular search filter option or options can be provided via a drop-down menu. For example, a usage option can be provided via which the user chooses a particular usage option, which might be Street, urban, or night. The lens option can provide the user, searching for a particular photo, to request photos that have been taken with a particular type of camera, such as a wide lens camera or a superwide lens camera. For example, an option can be provided in which the user indicates he or she would like to search for photos that were taken in a particular season of the year.

In accordance with at least one embodiment of the invention, “spot” generation can be correlated with the search filter options provided in the GUI 2100. For example, a patch can be processed to determine if the patch is associated with at least 20 pictures that were taken in the summer. If such patch does indeed include 20 pictures that were taken in the summer, then that patch would be deemed (by the photo system (PS)) to be a “spot” for that particular search criteria. More generally speaking, a particular area, such as a patch, can be assessed to determine if such area possesses density of photos with certain attributes, such as usage, lens, time of day, season, or crowd size. An area that does indeed possess density of a particular attribute can then be deemed a spot for that attribute. The user can then search for spots with such attribute, i.e. as shown in the GUI of FIG. 21.

FIG. 17 can also include the processing of step 764. In step 764, the PS interfaces with the user to input user location or user device location to apply in performing search for photos. For example, the PS can input the user device location in step 764. Then, additional processing can be requested by the user that uses such input user device location. For example, the “spots around me” processing can be performed after the PS inputs user device location in step 764.

FIG. 17 can also include the processing of step 765. In step 765, the PS performs “photo info” processing. FIG. 18 is a GUI 1800 that illustrates such processing. Further details of the GUI 1800 are described below.

In accordance with an embodiment, the PS can identify when the user has input search criteria and has selected that the PS should perform a search based on such search criteria. The search criteria can be a wide variety of criteria such as spots around the user, spots having a certain photo density, spots having photos of a particular type, spots having photos of a particular attribute, spots that are associated with a particular site, and other criteria as may be desired.

Accordingly, once the PS identifies that the user has interfaced (with the PS) so as to provide both search criteria and a request to perform the search, then the process passes onto step 766. In step 766, the PS outputs the results of the search to the user device. The results of the search can be one or more spots, from which the user can select, which match the input criteria. The results of the search can be one or more photos that match the input criteria. The results of the search can be one or more sites that match the input criteria. Additional processing can then be provided by the PS.

That is, in step 767, the PS can interface with the user device to determine if the user wants to refine the search criteria. If yes, then the process passes back to step 761. Processing then continues as described above. In step 768, the PS can interface with the user to determine if the user wants more information regarding an identified spot, for example. More specifically, the processing of step 768 can include a situation in which the user is presented with a spot or spots that matches the search criteria input by the user. Upon being presented with spots that match the search criteria, the user can select a particular spot. Upon selection of the particular spot, the PS can provide additional information to the user regarding the selected spot. FIG. 28 is a GUI 2800 that can be presented to the user—to provide a collection of spots that satisfy search criteria of the user. Upon selection of a particular spot in the GUI 2800, of FIG. 28, various information regarding the selected spot can be presented. Details of a selected spot that can be presented to the user includes number of photos in the spot, various attributes of those photos, the types of photos in the particular spot, attributes of users who took the photos that are in a particular spot, and various other information regarding the spot and photos that are associated with the spot.

In the processing of step 768, a yes request can be received. Accordingly, the process passes onto step 769. In step 769, the PS outputs further data, regarding the selected spot, to the user device.

As described above, FIG. 18 is a GUI that illustrates aspects of “photo info” processing. The GUI 1800 can be utilized or a variation of the GUI 1800, to perform a variety of processing. The GUI 1800 can be presented to the user in various operating situations. For example, the GUI 1800 can be presented to the user in conjunction with inputting a photo from the user. For example, the GUI 1800 can be utilized to provide the processing of step 741 of FIG. 16. The GUI 1800 can include a plurality of criteria 1801. For example, the criteria 1801 can constitute the “location type” of step 741 (FIG. 16). Each criteria can then be associated with a ranking 1802. The ranking 1802, for each criteria 1801, can be selected by the user so as to input data regarding attributes of the particular photo (that is being input or uploaded from the user). For example, in the example GUI 1800, the user has selected a particular ranking 1810 that corresponds to a “drink” location type. The user can tap such item 1810. Upon tapping such item 1810, the PS can highlight the item, bold the item, or provide some other change in appearance of the item so that the user can see that such item has been selected. The user can select any number of items in the GUI 1800. Additionally, the GUI 1800 can be provided with a button 1820. The button 1800 can take the user to additional GUIs or user interface mechanisms by which the user can input additional information regarding the particular photo. Accordingly, the GUI 1800 provides a quick and efficient way for the PS to input information regarding a photo from the user. Upon a user completing his or her selection in the GUI 1800, the user can tap the button 1840 so as to indicate to the PS that the user has completed selection—and that the PS can process the uploaded or input photo based on the user's selections.

Accordingly, the GUI 1800 of FIG. 18 can be utilized in conjunction with a user uploading a photo—to provide the user with a mechanism to input attributes of such uploaded photo. However, the GUI 1800 can also be utilized to perform other processing. That is, the GUI 1800 can be presented to the user so that the PS can input search criteria from the user. The user can make selections in the “matrix” of the GUI 1800 as may be desired. The PS can then perform a search based on the input criteria. In such processing, the button 1820 can be utilized by the user so as to allow the user to input more detailed search criteria information. In such processing, the button 1840 can be selected by the user upon the user completing his or her selection(s), for example ranking 1810, of the various items in the GUI 1800. Thus, upon the user tapping the button 1840, the PS can perform the requested search.

FIG. 19 shows a GUI 1900. The GUI 1900 can be presented to a user or potential user in conjunction with the user signing on or logging in to the PS (photo system). The GUI 1900 can include a login option. The GUI 1900 can include a login via Facebook option. The GUI 1900 can also include a create account option, i.e. in the situation that the user has not yet created an account in the PS. It should be appreciated the options shown in the GUI 1900 are illustrative. Additional options or mechanisms can be provided so as to input credentials from the user and allow the user access to his or her account.

Features of FIG. 20 are described above. In accordance with at least one embodiment of the disclosure, the GUI 2000 of FIG. 20 can provide a main landing page. That is, the user might be presented with the GUI 2000 upon first logging on to or in 2 the system.

As described above, a processing option provided by the PS can include “spots around me” or what might be described as “spots near me”. In such processing option, the PS can generate a GUI 2200 such as shown in FIG. 22. The GUI 2200 can include first indicia 2201 that reflects user location or user device location. The GUI 2200 can also include second indicia 2210 that reflects “spots” around the user that the PS has identified. For example, the user might hover his or her cursor over a particular indicia 2210 so as to invoke the system to present additional information regarding the particular spot that is thereby selected. A user can select a particular spot in some manner, such as by clicking the particular spot, as represented by indicia 2210. In clicking a particular spot, the PS can present the user with various photos 2220 that are contained in the selected spot. The PS can interface with the user so as to allow the user to “flip through” the various presented photos 2220.

As described herein, the PS can perform various processing related to a spot. A spot can be generated based on a particular area, such as a patch, having sufficient photo density. Relatedly, a plurality of spots can be collectively form a “site”. In such processing, the PS can generate a GUI 2300 such as shown in FIG. 23. The GUI 2300 can include first indicia 2301 that reflects user location or user device location. The GUI 2300 can also include second indicia 2310 that reflects “sites” around the user—that the PS has identified. The user might hover his or her cursor over a particular indicia 2310 so as to invoke the system to present additional information regarding the particular site that is thereby selected. A user can select a particular site in some manner, such as by clicking. In clicking the particular site, the PS can present the user with various photos 2320 that are contained in the particular selected site. The PS can interface with the user so as to allow the user to “flip through” the various presented photos 2320 that are associated with the selected site.

In accordance with a further aspect of the disclosure, FIG. 24 shows a GUI 2400 that illustrates “spots around me” functionality. The GUI 2400 can provide information to the user regarding spots around the user—and more specifically provide a map view of spots around the user. A user can select a particular spot, for example as represented by indicia 2450. Once selected, information regarding the particular spot can be displayed to the user in a display box 2460. A distance between the user and the spot can be displayed. For example, such distance might be based on a center point of the particular spot. As described above, a “spot” can be an area, such as a patch, that has attained a threshold density of photos or that has attained a threshold density of photos of a particular type or attribute, for example. The display box 2460 can also include additional information. Such additional information can include sites that are associated with the particular selected spot. The user might select a particular site so as to result in the PS to display yet further information regarding the particular site.

FIG. 25 shows an additional GUI 2500 that can be presented to the user to provide additional information to the user regarding spots around the user or user device. The selection of photos can be presented based on various options or criteria. For example, a representative photo for each spot can be presented to the user. For example, all photos from a particular spot or spots can be presented to the user. The user can be provided the ability to select a photo. Selection of the photo can result in additional information being displayed to the user, such as particulars of the spot to which the photo is associated. Such additional information is illustrated by display box 2650 in the GUI 2600 of FIG. 26. Such data can include a distance that the user is from the selected spot or the selected photo. Such data can include sites that are associated with the selected spot or the selected photo.

FIG. 27 shows a GUI 2700 that can be presented to the user via a suitable menu selection. The GUI 2700 can include various options including about, my account, my spots, following, and preferences. The about option can provide various information about the user. The my account option can provide various information to the user regarding her account, as well as functionality so that the user can change attributes of their account. For example, the my account option might provide the user with the ability to change a phone number associated with her account. The my spots option can provide various information regarding the spots, i.e. that can be described as “Spotz”, that are associated with the user account. For example, the my spots option can provide the user with details regarding spots to which the user has contributed photos. The my spots option can provide the user functionality so that the user can opt to receive updates regarding particular spots, i.e. favorite spots.

Additionally, the GUI 2700 can include a “following” option. The following option can provide functionality by which the user can select spots that the user desires to “follow”. For example, a user following a spot can mean that the system can identify any changes or updates to the followed spot. For example, if photos are added to the particular spot, then the user (who is following the spot) can be notified of such added photos. Additionally, the “following” functionality can include various other options. For example, the following functionality can include an association between the particular user and a second user. For example, a first user might follow a second user so as to be updated regarding where the second user has taken photos, spots with which the second user has engaged with, or other information. The PS can interface with each of the involved users so as to input authorization and/or acceptance to share related data.

As described above, FIG. 28 shows a GUI 2800 that can provide various information regarding one or more spots. In particular, FIG. 28 can provide the user access to photos associated with a particular spot.

As described above, FIG. 18 is a GUI (graphical user interface) 1800 that illustrates aspects of “photo info” processing. The GUI 1800 can provide an interface by which the PS can input various information regarding a photo, a group of photos, a collection of photos, a spot, or a site, for example. As described above, the GUI 1800 can also include the button 1820. The button 1820 can be selected by the user so as to input additional information. Relatedly, FIG. 30 shows a GUI 3000 that can be displayed as a result of the user tapping or selecting the button 1820. In other words, the GUI 3000 can be presented to the user so as to provide the user the ability to add additional information above and beyond that input via GUI 1800. The GUI 3000 can be presented to the user via suitable menu option. The GUI 3000 might be selected by the user tapping the criteria or location type “do” 1801 in the GUI 1800 of FIG. 18. The GUI 3000 allows the user to input additional particulars regarding the “do” location type. For example, if an input photo relates to activities associated with a museum, then the user might select the appropriate item 3060 as illustrated in FIG. 30. The GUI 3000 can include dialogue box 3099 into which a user can input comments, i.e. text, regarding the user's selection in the GUI 3000, one or more associated photos, and/or the user, for example.

FIG. 29 is a further GUI 2900 in accordance with principles of the disclosed subject matter. The GUI 2900 can be presented to the user in connection with either tagging a photo that is to be uploaded to the photo system or in connection with searching for a particular type of photo or spots having a particular type of photo. Using the GUI 2900, the user can specify whether the user wants to tag a particular photo as see, do, drink, eat, or stay, for example—or to search for a photo having such attributes.

FIG. 31 illustrates a further GUI 3100 that can be generated by the PS and presented to the user. The further GUI 3100 allows the user to input additional information regarding a “drink” location type. The GUI 3100 can include dialogue box 3199 into which a user can input comments, i.e. text, regarding the user's selection in the GUI 3100, one or more associated photos, and/or the user, for example. FIG. 32 illustrates a further GUI 3200 that can be generated by the PS and presented to the user. The further GUI 3200 allows the user to input additional information regarding an “eat” location type. The GUI 3200 can include dialogue box 3299 into which a user can input comments, i.e. text, regarding the user's selection in the GUI 3200, one or more associated photos, and/or the user, for example. FIG. 33 illustrates a further GUI 3300 that can be generated by the PS and presented to the user. The further GUI 3300 allows the user to input additional information regarding a “stay” location type. The GUI 3300 can include dialogue box 3399 into which a user can input comments, i.e. text, regarding the user's selection in the GUI 3300, one or more associated photos, and/or the user, for example.

Hereinafter further features of the systems and methods of the disclosure will be described.

As described above, a particular area can achieve a predetermined density of photos so that the area can be elevated to the status of a spot. The predetermined density of photos can include a determination of how many photos of any type are disposed in the particular area. The predetermined density of photos can include a determination of how many photos of a particular type are disposed in a particular area. In response to a search query by a user, search results can be provided based on whether an area has or has not attained the status of a spot. Further functionality can be provided so as to distinguish between different spots. For example, spots can be ranked so as to be compared with other spots. For example, a predetermined threshold to attain spot status can be 20 photos in a particular area, such as in a particular patch. However one spot can include 21 photos. Another spot can include 55 photos. Accordingly, functionality can be provided so as to differentiate the relevancy of such 2 different spots. For example, data can be provided to the user, in response to a search query, so as to advise the user of such different density in spots. For example, a spot can be ranked so as to be able to be compared with other spots.

Additionally, the criteria or thresholds used to determine if density of an area is sufficient to deem the area a “spot” can depend on various criteria. For example, in a highly populated area, the threshold to elevate an area to a spot can be different than the threshold (to elevate an area to a spot) in a very rural area. Thus, in New York City, a patch might be required to have 50 photos associated with a patch so as to attain spot status. On the other hand, a patch in a rural area may only be required to have 10 photos associated with such patch so as to attain spot status. Further, patches in respective regions, such as rural versus urban, can be of different size, in accordance with at least one embodiment of the disclosed subject matter.

Relatedly, various attributes of a particular photo can be used so as to determine whether the photo should or should not count toward elevating a particular area to a spot. For example, date data or metadata that is associated with a particular photo can dictate whether the photo should be counted towards elevating an area to spot status. For example, for a particular area, if the date of the photo is more than 6 weeks old, then the photo might not count. In a high-traffic area, such threshold date might be much more recent than a more rural area. Various factors can be considered in determining such threshold date for whether a photo is or is not counted towards spot status. Additionally, date “Windows” can be utilized. For example, a particular event may have occurred over a particular week. Accordingly, only photos that bear a date of that week might be deemed to count towards spot status. Additionally, attributes relating to upload of the photo can also be taken into account in whether a photo should or should not be counted towards spot status. For example, if a photo is taken at a particular location, in a particular area, and uploaded within 5 minutes—then such photo may be deemed a “recent” or “live” photo. In such processing, both data regarding when the photo was actually taken and when the photo was uploaded can be used. For example, if the photo was not uploaded until after some predetermined time, such as two days, then the photo might not be counted towards spot status. Accordingly, predetermined thresholds can be used that relate to when a photo was taken and when the photo was uploaded to the photo system, for example.

As described herein, a spot can be generated in any of a variety of manners. A spot can be generated based on pure number of photos within a particular area. A spot can be generated based on number of photos of a particular type within a particular area. Thus, a single geographical area can be associated with a plurality of spots that correspond to that area. For example, a particular area may be deemed a spot based on such area including 20 photos that have been tagged as location type “drink”. That same area may be deemed a spot based on such area including 20 photos that have been tagged as location type “eat”. Additionally, that same area may be deemed a spot based on such area including a total number of 30 photos, i.e. in the situation that a threshold number of photos to attain spot status might be 25. Accordingly, the PS provides the ability for a user to search or assess “spots” in a variety of different manners. Such different manners might be described as different “lenses” through which the user might look to assess details of a particular area. Relatedly, functionality provided by the PS may allow for the observation of correlation, or lack thereof, between attributes of spots associated with a particular area. For example, a particular “area X” may have gained spot status by virtue of a sufficient number of photos being tagged as location type “eat”. Indeed, the number of photos may have far exceeded the threshold to attain spot status. However, that same area X may not have attained spot status based on number of photos being tagged as location type “drink”. Accordingly, such disparity can be observed. In such situation, it may be the case, for some reason, that a correlation is expected between drink location type and location type. However, in this example, such correlation is not observed. Accordingly, such disparity may be flagged and appropriate action taken and/or appropriate research performed so as to determine the reason behind such disparity. Appropriate action can be taken in some automated manner by the photo system.

Relatedly, the upload or submission of a photo associated with a particular area may indeed constitute a “vote” by the user for that area. As the user uploads a further photo associated with an area, that photo constitutes a further vote for the area. Such functionality can be described as “your picture is your vote” or such functionality can be described as “the picture is your vote”.

In accordance with principles of the disclosed subject matter, a submitted photo can relate to various aspects of ranking and popularity. Popularity can include or relate to volume of submitted photos and/or a preference strength as determined by submitted photos and can be flexible for location type, etc. Therefore, a submitted photo by a user can led to related ranking processing and attributes, such as the ranking of a spot or area. Accordingly, a user's photo can constitute a vote and that vote can vary by location and/or purpose. The viewpoint of a “spot” can be presented in a variety of methods, whether by volume ranking, user following strength, affinity group, etc. Such processing can be described as an “assessment” that can include assessment of “ratings” based upon varying ranking viewpoints, different lenses, lenses of different rankings and dominant lens, for example.

To describe further, processing can be performed that provides an “assessment” of a spot or other area. Such “assessment” can include verification of attributes of an area, and such attributes can include popularity of an area. Assessment can include performing processing to provide multiple viewpoints of the same thing, such as the popularity of a coffee house based on input photos that are input from two different affinity groups. Assessment can reveal differing or divergent viewpoints of an area. Assessment can include the aggregation or analysis of an area from different perspectives or from different lenses or from different affinity groups, i.e. based on respective data that is input from such different affinity groups. Assessment can reveal both (1) validation of an attribute of an area and/or (2) identification of divergence of opinion regarding an attribute of an area.

For example, some users might be associated with a first affinity group and some users might be associated with a second affinity group. Association of a particular user to an affinity group can be based on user interaction and/or attributes of the user. For example, the user might input data to the system indicating that the user is a “hiker” or a “climber”. A GUI might be presented to the user via which the user inputs such data. Also, attributes of a user might dictate an affinity group to which the user will be associated, i.e. for example, the system might identify locations that the user frequents and, based thereon, tag the user as a hiker or a climber.

In one scenario, the hiker affinity group might collectively submit photos, which can be described as votes, so as to deem a particular restaurant popular. The climber affinity group might also collectively submit photos so as to deem the same restaurant popular. Based on such data that is input by the system, the system can assign a level of validation to such restaurant as truly being popular, i.e. since there was correlation between the hiker group and the climber group.

In a different scenario, the hiker affinity group might collectively submit photos, which can be described as votes, so as to deem a particular restaurant popular. The climber affinity group might also collectively submit photos so as to deem the same restaurant NOT popular. Based on such data that is input by the system, the system can assign a level of divergence or an indication of divergence to such restaurant as questionably being popular, i.e. since there was NOT correlation between the hiker group and the climber group.

Accordingly, “assessment” processing of the disclosure can (1) determine popularity of an area, (2) determine unpopularity of an area, and/or identify divergent perspectives of different affinity groups, for example. Assessment processing of the disclosure can include (1) determination of a popularity of an area, (2) validation of a popularity of an area, (3) substantiation of a popularity of an area, and/or (3) identify divergence (of popularity or unpopularity) amongst different viewpoints or amongst different affinity groups.

Such “assessment” might also be described as a “triangulation” of a spot or area or might also be described as including “triangulation” of a spot or area.

In accordance with principles of the disclosed subject matter and as described above, the world or planet can be divided into areas in accordance with principles of the disclosed subject matter. The areas can include 6 levels in accordance with one embodiment of the disclosed subject matter. The areas can be divided in a hierarchical manner—with each area of a particular level being divided into subareas. Such might be in the form of a parent and child interrelationship as described above. However, the disclosure is not limited to such particulars. For example, instead of the planet being broken down into areas and subareas, a venue might be broken into areas. For example, the venue of a tradeshow might be an area to be broken down, i.e. such that the venue of the tradeshow is analogous to the planet. The venue of a tradeshow might be broken down into different levels of areas as desired, such as 4 levels. The lowest level might be termed a “patch” akin to the patch described above. Each of the patches at the tradeshow might correspond to a respective booth. As each booth receives a threshold number of photos, that booth/patch is elevated to be a “spot”. Each photo can be viewed as a vote. The systems and methods of the disclosure can be applied in many other uses. For example, the systems and methods of the disclosure can be applied to zip codes and/or voting wards.

The systems and methods of the disclosure can also include functionality related to monitoring or censoring that can be performed by the photo system (PS) or by users of the PS. For example, such censoring can include a user censoring for inappropriate photo content or other content (for example explicit content or violence) being uploaded. Another example of censoring can include a user censoring for photos that have been tagged with an inaccurate or inappropriate location type. For example, a user might observe a number of photos that have been tagged as location type “places to eat”. However, upon review of such photos, the photos may not in any way be related to restaurants or eating. Accordingly, the user may interface with the system so as to de-tag or un-tag the particular photo or photos. In at least some embodiments, such un-tagging can result in the photo immediately being removed from such “places to eat” status. In other embodiments, an administration person or functionality may be required prior to the photo being removed or un-tagged from such “places to eat” status. In some embodiments, a user can be provided with the ability to quarantine a photo or a group of photos.

Relatedly, functionality can be provided so as to censor the censoror, i.e. the user doing the censoring. Such functionality can be provided by the photo system (PS) assessing correlations between various data or data sets. For example, a user that is observed as censoring outside or in excess of a norm can be scrutinized or constrained in some manner. For example, a user can be constrained based on some predetermined threshold(s). For example, if a user is observed by the system to de-tag or un-tag allegedly inappropriate photos at twice average rate—such might constitute a threshold. Based on exceeding such threshold, a user's ability to de-tag or un-tag additional photos might be disabled. Such disabling might be performed in some automated manner by the photo system. In accordance with principles of the disclosed subject matter, such a user can be identified as an outlier, based on predetermined criteria and/or thresholds, and as a result, the user's censoring abilities be constrained or disabled in some manner.

Systems and methods are provided to process a digital photo. An apparatus to process digital photos can include a tangibly embodied computer processor (CP) and a tangibly embodied database, the CP implementing instructions on a non-transitory computer medium disposed in the database, and the database in communication with the CP. The apparatus can include (A) a communication portion for providing communication between the CP and an electronic user device; (B) the database that includes the non-transitory computer medium, and the database including the instructions, and (C) a cascading framework that includes framework areas, and the framework areas include: first level areas, and each of the first level areas divided into second level areas, the second level areas being divided into third level areas; and (D) the CP. The CP can perform processing including: (a) inputting a photo from the user device, and the photo including geographic data that represents a photo location at which the photo item was generated; (b) comparing the first level area, of the first level areas, in which the photo location is located and associating a first level area identifier to the photo as part of the photo data; (c) comparing the photo location with the second level areas to determine a second level area in which the photo location is located and associating a second level area identifier to the photo as part of the photo data; (d) comparing the photo location with the third level areas to determine a matching third level area in which the photo location is located and associating a third level area identifier to the photo as part of the photo data; (e) assigning the photo to the matching third level area; and (f) performing photo processing, and the photo processing including aggregating a photo count of the matching third level area.

In accordance with principles of the disclosed subject matter, the disclosure provides systems and methods to perform geographic identification of an area combined with using a photo, which is associated with the area, as a vote for one or more popularity determinations of the geographic area. The geographic area can be used for a variety of other purposes. The geographic area and/or a photo associated with the geographic area can be tagged so as to associate content or attributes to the geographic area and/or to the photo.

Hereinafter, further aspects of the disclosure will be described.

As used herein, any term in the singular may be interpreted to be in the plural, and alternatively, any term in the plural may be interpreted to be in the singular.

It is appreciated that a feature of one embodiment of the disclosure as described herein may be used in conjunction with features of one or more other embodiments as may be desired.

Hereinafter, further aspects of implementation of the systems and methods of the disclosure will be described.

As described herein, at least some embodiments of the system of the disclosure and various processes, of embodiments, are described as being performed by one or more computer processors. Such one or more computer processors may be in the form of a “processing machine,” i.e. a tangibly embodied machine or an “apparatus”. As used herein, the term “processing machine” can be understood to include at least one processor that uses at least one memory. The at least one memory can store a set of instructions. The instructions may be either permanently or temporarily stored in the memory or memories of the processing machine. The processor can execute the instructions that are stored in the memory or memories in order to process data. The set of instructions may include various instructions that perform a particular task or tasks, such as any of the processing as described herein. Such a set of instructions for performing a particular task may be characterized as a program, software program, code or simply software.

As noted above, the processing machine, which may be constituted, for example, by the particular system and/or systems described above, executes the instructions that are stored in the memory or memories to process data. This processing of data may be in response to commands by a user or users of the processing machine, in response to previous processing, in response to a request by another processing machine and/or any other input, for example.

As noted above, the machine used to implement the disclosure may be in the form of a processing machine. The processing machine may also utilize (or be in the form of) any of a wide variety of other technologies including a special purpose computer, a computer system including a microcomputer, mini-computer or mainframe for example, a programmed microprocessor, a micro-controller, a peripheral integrated circuit element, a CSIC (Consumer Specific Integrated Circuit) or ASIC (Application Specific Integrated Circuit) or other integrated circuit, a logic circuit, a digital signal processor, a programmable logic device such as a FPGA, PLD, PLA or PAL, or any other device or arrangement of devices that can be capable of implementing the steps of the processes of the disclosure.

The processing machine used to implement the disclosure may utilize a suitable operating system. Thus, embodiments of the disclosure may include a processing machine running the Windows 10 operating system, the Windows 8 operating system, Microsoft Windows™ Vista™ operating system, the Microsoft Windows' XP™ operating system, the Microsoft Windows' NTTM operating system, the Windows' 2000 operating system, the Unix operating system, the Linux operating system, the Xenix operating system, the IBM AIX™ operating system, the Hewlett-Packard UX™ operating system, the Novell Netware™ operating system, the Sun Microsystems Solaris' operating system, the OS/2™ operating system, the BeOS™ operating system, the Macintosh operating system, the Apache operating system, an OpenStep™ operating system or another operating system or platform.

It is appreciated that in order to practice the method of the disclosure as described above, it is not necessary that the processors and/or the memories of the processing machine be physically located in the same geographical place. That is, each of the processors and the memories used by the processing machine may be located in geographically distinct locations and connected so as to communicate in any suitable manner. Additionally, it is appreciated that each of the processor and/or the memory may be composed of different physical pieces of equipment. Accordingly, it is not necessary that the processor be one single piece of equipment in one location and that the memory be another single piece of equipment in another location. That is, it is contemplated that the processor may be two pieces of equipment in two different physical locations. The two distinct pieces of equipment may be connected in any suitable manner. Additionally, the memory may include two or more portions of memory in two or more physical locations.

To explain further, processing as described above can be performed by various components and various memories. However, it is appreciated that the processing performed by two distinct components as described above may, in accordance with a further embodiment of the disclosure, be performed by a single component. Further, the processing performed by one distinct component as described above may be performed by two distinct components. In a similar manner, the memory storage performed by two distinct memory portions as described above may, in accordance with a further embodiment of the disclosure, be performed by a single memory portion. Further, the memory storage performed by one distinct memory portion as described above may be performed by two memory portions.

Further, as also described above, various technologies may be used to provide communication between the various processors and/or memories, as well as to allow the processors and/or the memories of the disclosure to communicate with any other entity; i.e., so as to obtain further instructions or to access and use remote memory stores, for example. Such technologies used to provide such communication might include a network, the Internet, Intranet, Extranet, LAN, an Ethernet, or any client server system that provides communication, for example. Such communications technologies may use any suitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions can be used in the processing of the disclosure on the processing machine, for example. The set of instructions may be in the form of a program or software. The software may be in the form of system software or application software, for example. The software might also be in the form of a collection of separate programs, a program module within a larger program, or a portion of a program module, for example. The software used might also include modular programming in the form of object oriented programming. The software tells the processing machine what to do with the data being processed.

Further, it is appreciated that the instructions or set of instructions used in the implementation and operation of the disclosure may be in a suitable form such that the processing machine may read the instructions. For example, the instructions that form a program may be in the form of a suitable programming language, which can be converted to machine language or object code to allow the processor or processors to read the instructions. That is, written lines of programming code or source code, in a particular programming language, can be converted to machine language using a compiler, assembler or interpreter. The machine language can be binary coded machine instructions that are specific to a particular type of processing machine, i.e., to a particular type of computer, for example. The computer understands the machine language.

A suitable programming language may be used in accordance with the various embodiments of the disclosure. Illustratively, the programming language used may include assembly language, Ada, APL, Basic, C, C++, COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX, Visual Basic, and/or JavaScript, for example. Further, it is not necessary that a single type of instructions or single programming language be utilized in conjunction with the operation of the system and method of the disclosure. Rather, any number of different programming languages may be utilized as can be necessary or desirable.

Also, the instructions and/or data used in the practice of the disclosure may utilize any compression or encryption technique or algorithm, as may be desired. An encryption module might be used to encrypt data. Further, files or other data may be decrypted using a suitable decryption module, for example.

As described above, the disclosure may illustratively be embodied in the form of a processing machine, including a computer or computer system, for example, that includes at least one memory. It is to be appreciated that the set of instructions, i.e., the software for example, that enables the computer operating system to perform the operations described above may be contained on any of a wide variety of media or medium, as desired. Further, the data that can be processed by the set of instructions might also be contained on any of a wide variety of media or medium. That is, the particular medium, i.e., the memory in the processing machine, utilized to hold the set of instructions and/or the data used in the disclosure may take on any of a variety of physical forms or transmissions, for example. Illustratively, as also described above, the medium may be in the form of paper, paper transparencies, a compact disk, a DVD, an integrated circuit, a hard disk, a floppy disk, an optical disk, a magnetic tape, a RAM, a ROM, a PROM, a EPROM, a wire, a cable, a fiber, communications channel, a satellite transmissions or other remote transmission, as well as any other medium or source of data that may be read by the processors of the disclosure.

Further, the memory or memories used in the processing machine that implements the disclosure may be in any of a wide variety of forms to allow the memory to hold instructions, data, or other information, as can be desired. Thus, the memory might be in the form of a database to hold data. The database might use any desired arrangement of files such as a flat file arrangement or a relational database arrangement, for example.

In the system and method of the disclosure, a variety of “user interfaces” may be utilized to allow a user to interface with the processing machine or machines that are used to implement the disclosure. As used herein, a user interface includes any hardware, software, or combination of hardware and software used by the processing machine that allows a user to interact with the processing machine. A user interface may be in the form of a dialogue screen for example. A user interface may also include any of a mouse, touch screen, keyboard, voice reader, voice recognizer, dialogue screen, menu box, list, checkbox, toggle switch, a pushbutton or any other device that allows a user to receive information regarding the operation of the processing machine as it processes a set of instructions and/or provide the processing machine with information. Accordingly, the user interface can be any device that provides communication between a user and a processing machine. The information provided by the user to the processing machine through the user interface may be in the form of a command, a selection of data, or some other input, for example.

As discussed above, a user interface can be utilized by the processing machine that performs a set of instructions such that the processing machine processes data for a user. The user interface can be typically used by the processing machine for interacting with a user either to convey information or receive information from the user. However, it should be appreciated that in accordance with some embodiments of the system and method of the disclosure, it is not necessary that a human user actually interact with a user interface used by the processing machine of the disclosure. Rather, it is also contemplated that the user interface of the disclosure might interact, i.e., convey and receive information, with another processing machine, rather than a human user. Accordingly, the other processing machine might be characterized as a user. Further, it is contemplated that a user interface utilized in the system and method of the disclosure may interact partially with another processing machine or processing machines, while also interacting partially with a human user.

In this disclosure, quotation marks, such as with the language “spot”, have been used to enhance readability and/or to parse out a term or phrase for clarity.

It will be appreciated that features, elements and/or characteristics described with respect to one embodiment of the disclosure may be variously used with other embodiments of the disclosure as may be desired.

It will be appreciated that the effects of the present disclosure are not limited to the above-mentioned effects, and other effects, which are not mentioned herein, will be apparent to those in the art from the disclosure and accompanying claims.

Although the preferred embodiments of the present disclosure have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the disclosure and accompanying claims.

As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, third, etc., may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are only used to distinguish one element, component, process step, region, layer or section from another region, layer or section. Thus, a first element, component, process step, region, layer or section could be termed a second element, component, process step, region, layer or section without departing from the teachings of the present disclosure.

Spatially and organizationally relative terms, such as “lower”, “upper”, “top”, “bottom”, “left”, “right” and the like, may be used herein for ease of description to describe the relationship of one element or feature to another element(s) or feature(s) as illustrated in the drawing figures. It will be understood that spatially and organizationally relative terms are intended to encompass different orientations of or organizational aspects of components in use or in operation, in addition to the orientation or particular organization depicted in the drawing figures.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, process steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, process steps, operations, elements, components, and/or groups thereof.

Embodiments of the disclosure are described herein with reference to diagrams, flowcharts and/or other illustrations, for example, that are schematic illustrations of idealized embodiments (and intermediate components) of the disclosure. As such, variations from the illustrations are to be expected. Thus, embodiments of the disclosure should not be construed as limited to the particular organizational depiction of components and/or processing illustrated herein but are to include deviations in organization of components and/or processing.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Any reference in this specification to “one embodiment,” “an embodiment,” “example embodiment,” etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment. Further, as otherwise noted herein, when a particular feature, structure, or characteristic is described in connection with any embodiment, it is submitted that it is within the purview of one skilled in the art to effect and/or use such feature, structure, or characteristic in connection with other ones of the embodiments.

While the subject matter has been described in detail with reference to exemplary embodiments thereof, it will be apparent to one skilled in the art that various changes can be made, and equivalents employed, without departing from the scope of the disclosure.

All references and/or documents referenced herein are hereby incorporated by reference in their entirety.

It will be readily understood by those persons skilled in the art that the present disclosure is susceptible to broad utility and application. Many embodiments and adaptations of the present disclosure other than those herein described, as well as many variations, modifications and equivalent arrangements, will be apparent from or reasonably suggested by the present disclosure and foregoing description thereof, without departing from the substance or scope of the disclosure.

Accordingly, while the present disclosure has been described here in detail in relation to its exemplary embodiments, it is to be understood that this disclosure is only illustrative and exemplary of the present disclosure and is made to provide an enabling disclosure of the disclosure. Accordingly, the foregoing disclosure is not intended to be construed or to limit the present disclosure or otherwise to exclude any other such embodiments, adaptations, variations, modifications and equivalent arrangements. 

What is claimed is:
 1. An apparatus to process digital photos, the apparatus including a tangibly embodied computer processor (CP) and a tangibly embodied database, the CP implementing instructions on a non-transitory computer medium disposed in the database, and the database in communication with the CP, the apparatus comprising: a communication portion for providing communication between the CP and an electronic user device; the database that includes the non-transitory computer medium, and the database including the instructions, and a cascading framework that includes framework areas, and the framework areas include: first level areas, and each of the first level areas divided into second level areas, the second level areas being divided into third level areas; and the CP, and the CP performing processing including: inputting a photo from the user device, and the photo including geographic data that represents a photo location at which the photo was generated; comparing the first level area, of the first level areas, in which the photo location is located and associating a first level area identifier to the photo as part of photo data; comparing the photo location with the second level areas to determine a second level area in which the photo location is located and associating a second level area identifier to the photo as part of the photo data; comparing the photo location with the third level areas to determine a matching third level area in which the photo location is located and associating a third level area identifier to the photo as part of the photo data; assigning the photo to the matching third level area; and performing photo processing, and the photo processing including aggregating a photo count of the matching third level area.
 2. The apparatus of claim 1, the third level areas each constitute a patch, and the third level areas each being a smallest area of the framework.
 3. The apparatus of claim 1, the framework areas further including fourth level areas, which are areas divided from the third level areas and associating a fourth level area identifier to the photo as part of the photo data.
 4. The apparatus of claim 3, the framework areas further including fifth level areas, which are areas divided from the fourth level areas and associating a fifth level area identifier to the photo as part of the photo data.
 5. The apparatus of claim 4, the framework areas further including sixth level areas, which are areas divided from the fifth level areas and associating a sixth level area identifier to the photo as part of the photo data.
 6. The apparatus of claim 1, the CP performing further processing that includes generating framework areas of the cascading framework.
 7. The apparatus of claim 1, the comparing the photo location with the second level areas includes: only comparing the photo location with the second level areas that belong to the matching first level area; and the comparing the photo location with the third level areas includes: only comparing the photo location with the third level areas that belong to the matching second level area.
 8. The apparatus of claim 1, each photo includes image data and photo data, and the image data representing an image of the photo; and the photo data including geolocation and date that the picture was taken.
 9. The apparatus of claim 8, the photo data associated with a photo includes location type.
 10. The apparatus of claim 8, the photo data associated with a photo includes preference strength.
 11. The apparatus of claim 8, the photo data associated with a photo includes user affinity group.
 12. The apparatus of claim 8, the photo data associated with a photo includes user following strength.
 13. The apparatus of claim 8, the photo data associated with a photo includes the ability for users to flag items to identify the photo as “material of user concern”.
 14. The apparatus of claim 1, the cascading framework being populated with photos in the framework areas, and each of such photos being associated with photo data; and the CP performing further processing including: interfacing with a second user device via the communication portion; inputting search request data from the second user device; comparing the search request data with photo data of photo items in the cascading framework; identifying a matching photo item, of the photo items, that is associated with photo data that matches with the search request data; and outputting at least a portion of the matching photo item to the second user device.
 15. The apparatus of claim 1, the performing photo processing based on the assigning further includes: aggregating a photo count of the matching first level area; aggregating a photo count of the matching second level area; and aggregating a photo count of the matching third level area.
 16. The apparatus of claim 1, the cascading framework being populated with photos in the framework areas, and each of such photos being associated with photo data; and the CP performing further processing including: interfacing with a second user device via the communication portion; inputting user geolocation data from the second user device; comparing the user geolocation data with photo data of photo items in the cascading framework; identifying a third level area that is within a predetermined proximity to the user geolocation data; identifying photo items that are associated with the identified third level area; and outputting the identified photo items to the second user device.
 17. The apparatus of claim 16, the predetermined proximity being that the user geolocation data is within boundaries of the identified third level area; and the user geolocation data includes (a) a current location of the user device of the user and/or (b) a location selected by the user.
 18. The apparatus of claim 1, the performing photo processing further including determining a popularity, of each of the third level areas, based on a number of photos that are associated with each third level area and related photo data.
 19. The apparatus of claim 1, wherein patches being the smallest area of the framework, and wherein patches can be accumulated from at least one selected from the group consisting of: bottom up processing into a site, a predetermined known area, a popular location, a Zip code, and a voting ward. 